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2311.01343 | 15 | # 2.2 LLM in Recommender Systems
Recently, LLM-based RS has attracted extensive attention from both academia and industry, which are promising to address the long- standing issues of traditional ID-based RSs, such as shallow textual information understanding, poor generalization, etc. [34, 35]. Hou et al. showed that existing LLMs can be viewed as zero-shot rankers,
Collaborative Large Language Model for Recommender Systems | 2311.01343#15 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 15,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 2.2 LLM in Recommender Systems\nRecently, LLM-based RS has attracted extensive attention from both academia and industry, which are promising to address the long- standing issues of traditional ID-based RSs, such as shallow textual information understanding, poor generalization, etc. [34, 35]. Hou et al. showed that existing LLMs can be viewed as zero-shot rankers,\nCollaborative Large Language Model for Recommender Systems",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 15 | # 9. Ethical Considerations
The expanding use of large language models (LLMs), especially within mental healthcare, calls for thoughtful ethical engagement. As these models advance in generating responses that mirror human counselors, it is imperative we closely examine their impact on users, particularly those navigating mental health challenges.
# References
Ahn, Y., Zhang, Y., Park, Y., & Lee, J. (2020). A chatbot solution to chat app problems: Envisioning a chatbot counseling system for teenage victims of online sexual exploitation. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1â7).
American Psychiatric Association, American Psychiatric Association, (1994). Diagnostic and statistical manual of mental disorders: DSM-IV, volume 4. American Psychiatric Association, Washington, DC.
Anderson, C., & Keltner, D. (2002). The role of empathy in the formation and maintenance of social bonds. Behavioral and Brain Sciences, 25(1), 21â22.
Beck, A. T. (1979). Cognitive therapy and the emotional disorders. Penguin.
Bommarito II, M., & Katz, D. M. (2022). Gpt takes preprint exam. bar the arXiv:2212.14402. | 2311.04915#15 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 15,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 9. Ethical Considerations\nThe expanding use of large language models (LLMs), especially within mental healthcare, calls for thoughtful ethical engagement. As these models advance in generating responses that mirror human counselors, it is imperative we closely examine their impact on users, particularly those navigating mental health challenges.\n# References\nAhn, Y., Zhang, Y., Park, Y., & Lee, J. (2020). A chatbot solution to chat app problems: Envisioning a chatbot counseling system for teenage victims of online sexual exploitation. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1â7).\nAmerican Psychiatric Association, American Psychiatric Association, (1994). Diagnostic and statistical manual of mental disorders: DSM-IV, volume 4. American Psychiatric Association, Washington, DC.\nAnderson, C., & Keltner, D. (2002). The role of empathy in the formation and maintenance of social bonds. Behavioral and Brain Sciences, 25(1), 21â22.\nBeck, A. T. (1979). Cognitive therapy and the emotional disorders. Penguin.\nBommarito II, M., & Katz, D. M. (2022). Gpt takes preprint exam. bar the arXiv:2212.14402.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 16 | which can rank the relevance of movies based on user historical in- teractions and movie descriptions. However, since pretrained LLMs are not aligned with the recommendation task, more efforts have been devoted to the finetuning of LLMs to obtain recommendation- oriented models. An exemplar work is P5 [20], which finetunes T5 with token sequences transformed from interactions and user/item features, where items are presented by pseudo-IDs in the form of "item_ð". Afterwards, M6 [19] was proposed that combines text infill- ing and auto-regression in the pretraining stage, where pseudo IDs in P5 are completely avoided and replaced by textual descriptions. Recently, TALLRec [36] was proposed where items are represented by both pseudo-ID and textual descriptions. Pseudo-ID-based item representations can easily introduce spurious correlations between irrelevant items. To address this issue, Hua et al. proposed to intro- duce a small number of new tokens, where tokens used to describe the items are determined by their content and collaborative similar- ity. However, representing items with multiple shared tokens can still introduce bias. In addition, for the above | 2311.01343#16 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 16,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "which can rank the relevance of movies based on user historical in- teractions and movie descriptions. However, since pretrained LLMs are not aligned with the recommendation task, more efforts have been devoted to the finetuning of LLMs to obtain recommendation- oriented models. An exemplar work is P5 [20], which finetunes T5 with token sequences transformed from interactions and user/item features, where items are presented by pseudo-IDs in the form of \"item_ð\". Afterwards, M6 [19] was proposed that combines text infill- ing and auto-regression in the pretraining stage, where pseudo IDs in P5 are completely avoided and replaced by textual descriptions. Recently, TALLRec [36] was proposed where items are represented by both pseudo-ID and textual descriptions. Pseudo-ID-based item representations can easily introduce spurious correlations between irrelevant items. To address this issue, Hua et al. proposed to intro- duce a small number of new tokens, where tokens used to describe the items are determined by their content and collaborative similar- ity. However, representing items with multiple shared tokens can still introduce bias. In addition, for the above",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 16 | Bommarito II, M., & Katz, D. M. (2022). Gpt takes preprint exam. bar the arXiv:2212.14402.
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Bohg, J. (2021). On the opportunities and risks of foundation preprint arXiv:2108.07258.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Sastry, G. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877â1901.
Buechel, S., Buffone, A., Slaff, B., Ungar, L., & Sedoc, J. (2018). Modeling empathy and distress in reaction to news stories. arXiv preprint arXiv:1808.10399.
Cooper, M., & McLeod, J. (2011). Person- centered therapy: A pluralistic perspective. Experiential Person-Centered Psychotherapies, 10(3), 210â223.
Davis, M. H. (1980). Interpersonal reactivity index. | 2311.04915#16 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 16,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Bommarito II, M., & Katz, D. M. (2022). Gpt takes preprint exam. bar the arXiv:2212.14402.\nBommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Bohg, J. (2021). On the opportunities and risks of foundation preprint arXiv:2108.07258.\nBrown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Sastry, G. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877â1901.\nBuechel, S., Buffone, A., Slaff, B., Ungar, L., & Sedoc, J. (2018). Modeling empathy and distress in reaction to news stories. arXiv preprint arXiv:1808.10399.\nCooper, M., & McLeod, J. (2011). Person- centered therapy: A pluralistic perspective. Experiential Person-Centered Psychotherapies, 10(3), 210â223.\nDavis, M. H. (1980). Interpersonal reactivity index.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 17 | items are determined by their content and collaborative similar- ity. However, representing items with multiple shared tokens can still introduce bias. In addition, for the above methods, candidate items need to be explicitly provided in the prompt when conducting direct recommendation, where the size of candidate pool is limited. Finally, recommendations are generated via autoregression, which is highly inefficient. In summary, the dichotomy between natural language processing and RS still remains to be well addressed. | 2311.01343#17 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 17,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "items are determined by their content and collaborative similar- ity. However, representing items with multiple shared tokens can still introduce bias. In addition, for the above methods, candidate items need to be explicitly provided in the prompt when conducting direct recommendation, where the size of candidate pool is limited. Finally, recommendations are generated via autoregression, which is highly inefficient. In summary, the dichotomy between natural language processing and RS still remains to be well addressed.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 17 | Davis, M. H. (1980). Interpersonal reactivity index.
Davis, M. H. (1983). Measuring individual for a differences multidimensional of personality and social psychology, 44(1), 113.
De Vignemont, F., & Singer, T. (2006). The
empathic brain: How, when, and why? Trends in Cognitive Sciences, 10(10), 435â441.
Diehl, J. J., Schmitt, L. M., Villano, M., & Crowell, C. R. (2012). The clinical use of robots for individuals with autism spectrum disorders: A critical review. Research in autism spectrum disorders, 6(1), 249â262.
Eisenberg, N. (2014). Altruistic emotion, cognition, and behavior (PLE: Emotion). Psychology Press.
Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal of personality and social psychology, 17(2), 124.
Hall, J. A., & Schwartz, R. (2019). Empathy present and future. The Journal of social psychology, 159(3), 225â243.
Hofmann, S. G., Sawyer, A. T., & Fang, A. (2010). The empirical status of the "new wave" of cognitive behavioral therapy. Psychiatric Clinics, 33(3), 701â710. | 2311.04915#17 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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},
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"id": "2306.08997"
},
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"id": "2303.13375"
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"id": "1808.10399"
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 17,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Davis, M. H. (1980). Interpersonal reactivity index.\nDavis, M. H. (1983). Measuring individual for a differences multidimensional of personality and social psychology, 44(1), 113.\nDe Vignemont, F., & Singer, T. (2006). The\nempathic brain: How, when, and why? Trends in Cognitive Sciences, 10(10), 435â441.\nDiehl, J. J., Schmitt, L. M., Villano, M., & Crowell, C. R. (2012). The clinical use of robots for individuals with autism spectrum disorders: A critical review. Research in autism spectrum disorders, 6(1), 249â262.\nEisenberg, N. (2014). Altruistic emotion, cognition, and behavior (PLE: Emotion). Psychology Press.\nEkman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal of personality and social psychology, 17(2), 124.\nHall, J. A., & Schwartz, R. (2019). Empathy present and future. The Journal of social psychology, 159(3), 225â243.\nHofmann, S. G., Sawyer, A. T., & Fang, A. (2010). The empirical status of the \"new wave\" of cognitive behavioral therapy. Psychiatric Clinics, 33(3), 701â710.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.04915 | 18 | Kaczkurkin, A. N., & Foa, E. B. (2022). Cognitive-behavioral for anxiety disorders: An update on the empirical evidence. Dialogues in Clinical Neuroscience.
Knutson, D., & Koch, J. M. (2022). Person- centered therapy as applied to work with transgender and gender diverse clients. Journal of Humanistic Psychology, 62(1), 104â122.
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are preprint zero-shot arXiv:2205.11916.
Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
Linehan, M. M. (1987). Dialectical behavioral therapy: A cognitive behavioral approach to parasuicide. Journal of Personality Disorders, 1(4), 328â333.
Medeiros, L., Bosse, T., & Gerritsen, C. (2021). Can a chatbot comfort humans? studying the impact of a supportive chatbot on users' self- perceived IEEE Transactions on Human-Machine Systems, 52(3), 343â353.
Miller, W. R., & Rollnick, S. Motivational change. Guilford Press. | 2311.04915#18 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 18,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Kaczkurkin, A. N., & Foa, E. B. (2022). Cognitive-behavioral for anxiety disorders: An update on the empirical evidence. Dialogues in Clinical Neuroscience.\nKnutson, D., & Koch, J. M. (2022). Person- centered therapy as applied to work with transgender and gender diverse clients. Journal of Humanistic Psychology, 62(1), 104â122.\nKojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are preprint zero-shot arXiv:2205.11916.\nLazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.\nLinehan, M. M. (1987). Dialectical behavioral therapy: A cognitive behavioral approach to parasuicide. Journal of Personality Disorders, 1(4), 328â333.\nMedeiros, L., Bosse, T., & Gerritsen, C. (2021). Can a chatbot comfort humans? studying the impact of a supportive chatbot on users' self- perceived IEEE Transactions on Human-Machine Systems, 52(3), 343â353.\nMiller, W. R., & Rollnick, S. Motivational change. Guilford Press.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 19 | In this paper, we focus on recommendations with implicit feedback [37]. Consider a system of ð¼ users and ð½ items. We use a binary rating vector rð â {0, 1}ð½ to denote whether user ð has interacted with the ð½ items. In addition, we use xð¢ ð , xð£ ð to denote the textual features associated with user ð and item ð, such as user biography and item content, etc. xð¢ð£ ð ð denotes the textual features associated with both user ð and item ð, such as user ðâs review for item ð. Hereafter, {ð¢,ð£,ð¢ð£ } {ð¢,ð£,ð¢ð£ } {ð,ð,ð ð },ð is a size ð we take a sequential view of x {ð,ð,ð ð } , where x one-hot vector denoting the ðth token in the textual sequence2. In addition, we have a pretrained large language model (LLM), of which we take a | 2311.01343#19 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 19,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "In this paper, we focus on recommendations with implicit feedback [37]. Consider a system of ð¼ users and ð½ items. We use a binary rating vector rð â {0, 1}ð½ to denote whether user ð has interacted with the ð½ items. In addition, we use xð¢ ð , xð£ ð to denote the textual features associated with user ð and item ð, such as user biography and item content, etc. xð¢ð£ ð ð denotes the textual features associated with both user ð and item ð, such as user ðâs review for item ð. Hereafter, {ð¢,ð£,ð¢ð£ } {ð¢,ð£,ð¢ð£ } {ð,ð,ð ð },ð is a size ð we take a sequential view of x {ð,ð,ð ð } , where x one-hot vector denoting the ðth token in the textual sequence2. In addition, we have a pretrained large language model (LLM), of which we take a",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 19 | Miller, W. R., & Rollnick, S. Motivational change. Guilford Press.
(2003). Self-compassion: An Neff, K. alternative conceptualization of a healthy attitude toward oneself. Self and Identity, 2(2), 85â101.
Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). Capabilities of gpt-4 on medical challenge problems. arXiv preprint arXiv:2303.13375.
Nwosu, A., Boardman, S., Husain, M. M., & Doraiswamy, P. M. (2022). Digital therapeutics for mental health: Is attrition the Achilles heel? Frontiers in Psychiatry, 1598.
Prystawski, B., Thibodeau, P., & Goodman, N. (2022). Psychologically-informed chain-of- thought prompts for metaphor understanding in large language models. arXiv preprint arXiv:2209.08141. | 2311.04915#19 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
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"id": "2305.10601"
},
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"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
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"id": "1808.10399"
}
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 19,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Miller, W. R., & Rollnick, S. Motivational change. Guilford Press.\n(2003). Self-compassion: An Neff, K. alternative conceptualization of a healthy attitude toward oneself. Self and Identity, 2(2), 85â101.\nNori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). Capabilities of gpt-4 on medical challenge problems. arXiv preprint arXiv:2303.13375.\nNwosu, A., Boardman, S., Husain, M. M., & Doraiswamy, P. M. (2022). Digital therapeutics for mental health: Is attrition the Achilles heel? Frontiers in Psychiatry, 1598.\nPrystawski, B., Thibodeau, P., & Goodman, N. (2022). Psychologically-informed chain-of- thought prompts for metaphor understanding in large language models. arXiv preprint arXiv:2209.08141.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 20 | one-hot vector denoting the ðth token in the textual sequence2. In addition, we have a pretrained large language model (LLM), of which we take a probabilistic view and denote it as ðððð (xð+1|x1:ð ), (ð¿) 1:ð â Rð Ãð¾â via which transform x1:ð into a latent sequence h (ð¿) ð¿ stacked self-attention modules ððð(x1:ð ) and maps the h to ð the probability space of the next token xð+1. Since the LLM is pretrained on large corpora and finetuned on exemplar prompt- answer pairs, the generation is based on logical reasoning with the context information in x1:ð according to its pretrained knowledge. Our aim is to design a new RS that tightly couples the LLM with the recommendation task by introducing user/item ID tokens (and token embeddings), such that user/item semantics (e.g., user inter- ests in item) can be accurately modeled for effective and efficient recommendation whereas | 2311.01343#20 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 20,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "one-hot vector denoting the ðth token in the textual sequence2. In addition, we have a pretrained large language model (LLM), of which we take a probabilistic view and denote it as ðððð (xð+1|x1:ð ), (ð¿) 1:ð â Rð Ãð¾â via which transform x1:ð into a latent sequence h (ð¿) ð¿ stacked self-attention modules ððð(x1:ð ) and maps the h to ð the probability space of the next token xð+1. Since the LLM is pretrained on large corpora and finetuned on exemplar prompt- answer pairs, the generation is based on logical reasoning with the context information in x1:ð according to its pretrained knowledge. Our aim is to design a new RS that tightly couples the LLM with the recommendation task by introducing user/item ID tokens (and token embeddings), such that user/item semantics (e.g., user inter- ests in item) can be accurately modeled for effective and efficient recommendation whereas",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 20 | Rashkin, H., Smith, E. M., Li, M., & Boureau, Y-L. (2019). Towards empathetic open-domain conversation models: A new benchmark and dataset. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 5370â5381). Association for Computational Linguistics.
Rasouli, S., Gupta, G., Nilsen, E., & Dautenhahn, K. (2022). Potential applications of social robots in robot-assisted interventions for social anxiety. International Journal of Social Robotics, 14(5), 1â32.
Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y-L., & Weston, J. (2021). Recipes for building an open-domain chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. for Computational 300â325). Association Linguistics.
Scherer, K. R., Banse, R., & Wallbott, H. G. from vocal (2001). Emotion expression correlate across languages and cultures. Journal of Cross-cultural psychology, 32(1), 76â92. | 2311.04915#20 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 20,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Rashkin, H., Smith, E. M., Li, M., & Boureau, Y-L. (2019). Towards empathetic open-domain conversation models: A new benchmark and dataset. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 5370â5381). Association for Computational Linguistics.\nRasouli, S., Gupta, G., Nilsen, E., & Dautenhahn, K. (2022). Potential applications of social robots in robot-assisted interventions for social anxiety. International Journal of Social Robotics, 14(5), 1â32.\nRoller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y-L., & Weston, J. (2021). Recipes for building an open-domain chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. for Computational 300â325). Association Linguistics.\nScherer, K. R., Banse, R., & Wallbott, H. G. from vocal (2001). Emotion expression correlate across languages and cultures. Journal of Cross-cultural psychology, 32(1), 76â92.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.04915 | 21 | Sharma, A., Miner, A., Atkins, D., & Althoff, T. (2020). A to computational understanding empathy expressed in text-based mental health support. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 5263â5276). Association for Computational
Linguistics.
Sivarajkumar, S., Kelley, M., Samolyk- Mazzanti, A., Visweswaran, S., & Wang, Y. (2023). An empirical evaluation of prompting strategies for large language models in zero- shot clinical natural language processing.
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., & Hashimoto, T. B. replicable instruction-following model. Stanford Center for Research on Foundation Models. [Online]. at Available https://crfm.stanford.edu/2023/03/13/alpaca.html | 2311.04915#21 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 21,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Sharma, A., Miner, A., Atkins, D., & Althoff, T. (2020). A to computational understanding empathy expressed in text-based mental health support. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 5263â5276). Association for Computational\nLinguistics.\nSivarajkumar, S., Kelley, M., Samolyk- Mazzanti, A., Visweswaran, S., & Wang, Y. (2023). An empirical evaluation of prompting strategies for large language models in zero- shot clinical natural language processing.\nTaori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., & Hashimoto, T. B. replicable instruction-following model. Stanford Center for Research on Foundation Models. [Online]. at Available https://crfm.stanford.edu/2023/03/13/alpaca.html",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 22 | # 3.2 Extension of User/Item Tokens
3.2.1 Vocab Expansion. To tightly couple the pretrained LLM with the recommendation task, we first expand the vocabulary of
2we use ð¢ and ð£ in the superscript to distinguish user or item-related variables.
Conferenceâ17, July 2017, Washington, DC, USA
Vocab Pred. Head t Shared Pretrained LLM Backbone q Ivem Pred. Head Collab LLM, <user_i> has interacted with <item_j> <item_> <item_l>
Figure 2: The overview of the proposed CLLM4Rec in the mutually-regularized pretraining stage. Mutual regulariza- tion of item_k is omitted for simplicity.
the LLM by adding user/item ID tokens to describe the intrinsic user/item semantic, such that semantic gap between RS and natural language can be well bridged. We use bracket notations "<user_ð>" and "<item_ð>" to denote the newly-introduced token for the ðth user and the ðth item, respectively, which has token ID ð + ð and ð + ð¼ + ð, and will not be broken down into atomic tokens. | 2311.01343#22 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 22,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 3.2 Extension of User/Item Tokens\n3.2.1 Vocab Expansion. To tightly couple the pretrained LLM with the recommendation task, we first expand the vocabulary of\n2we use ð¢ and ð£ in the superscript to distinguish user or item-related variables.\nConferenceâ17, July 2017, Washington, DC, USA\nVocab Pred. Head t Shared Pretrained LLM Backbone q Ivem Pred. Head Collab LLM, <user_i> has interacted with <item_j> <item_> <item_l>\nFigure 2: The overview of the proposed CLLM4Rec in the mutually-regularized pretraining stage. Mutual regulariza- tion of item_k is omitted for simplicity.\nthe LLM by adding user/item ID tokens to describe the intrinsic user/item semantic, such that semantic gap between RS and natural language can be well bridged. We use bracket notations \"<user_ð>\" and \"<item_ð>\" to denote the newly-introduced token for the ðth user and the ðth item, respectively, which has token ID ð + ð and ð + ð¼ + ð, and will not be broken down into atomic tokens.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 22 | Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M-A., Lacroix, T., Roziere, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
Truax, C. B., & Carkhuff, R. (2007). Toward effective and psychotherapy: Training and practice. Transaction Publishers.
Urakami, J., Moore, B. A., Sutthithatip, S., & Park, S. (2019). Users' perception of empathic expressions by an advanced intelligent system. In Proceedings of the 7th International Conference on Human-Agent Interaction (pp. 11â18).
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain of thought prompting elicits reasoning in large language preprint arXiv:2201.11903.
Wondra, J. D., & Ellsworth, P. C. (2015). An appraisal theory of empathy and other vicarious emotional experiences. Psychological review, 122(3), 411. | 2311.04915#22 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 22,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M-A., Lacroix, T., Roziere, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.\nTruax, C. B., & Carkhuff, R. (2007). Toward effective and psychotherapy: Training and practice. Transaction Publishers.\nUrakami, J., Moore, B. A., Sutthithatip, S., & Park, S. (2019). Users' perception of empathic expressions by an advanced intelligent system. In Proceedings of the 7th International Conference on Human-Agent Interaction (pp. 11â18).\nWei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain of thought prompting elicits reasoning in large language preprint arXiv:2201.11903.\nWondra, J. D., & Ellsworth, P. C. (2015). An appraisal theory of empathy and other vicarious emotional experiences. Psychological review, 122(3), 411.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 23 | 3.2.2 Token Embeddings. For LLMs to understand the tokens, they must be first transformed into dense embeddings. Accordingly, we use zð¡ ð â ð
ð¾ to represent the pretrained embedding of the ðth vocab token. In addition, for the newly-introduced user/item tokens, we introduce two types of embeddings to represent user/item col- laborative and content semantics. Specifically, to align the user/item tokens with the vocab space of the pretrained LLM, we sample the user/item collaborative token embeddings from the same size-ð¾ latent space as follows:
aa? ~ N (0. ay! âIk), (1)
where A; is the prior precision for at 2? Importantly, to align the content semantics with the collaborative semantic for more recommendation-oriented content modeling, we sample the user/item content token embeddings from the following conditional prior: | 2311.01343#23 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 23,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.2.2 Token Embeddings. For LLMs to understand the tokens, they must be first transformed into dense embeddings. Accordingly, we use zð¡ ð â ð
ð¾ to represent the pretrained embedding of the ðth vocab token. In addition, for the newly-introduced user/item tokens, we introduce two types of embeddings to represent user/item col- laborative and content semantics. Specifically, to align the user/item tokens with the vocab space of the pretrained LLM, we sample the user/item collaborative token embeddings from the same size-ð¾ latent space as follows:\naa? ~ N (0. ay! âIk), (1)\nwhere A; is the prior precision for at 2? Importantly, to align the content semantics with the collaborative semantic for more recommendation-oriented content modeling, we sample the user/item content token embeddings from the following conditional prior:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 23 | Wondra, J. D., & Ellsworth, P. C. (2015). An appraisal theory of empathy and other vicarious emotional experiences. Psychological review, 122(3), 411.
Wubbolding, R. E., Casstevens, W. J., & Fulkerson, M. H. (2017). Using the wdep system of reality therapy to support person- treatment planning. Journal of centered Counseling & Development, 95(4), 472â477.
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601.
Zaki, J. (2019). The war for kindness: Building empathy in a fractured world. Crown.
Zhang, S. J., Florin, S., Lee, A. N., Niknafs, E., Marginean, A., Wang, A., Tyser, K., Chin, Z., Hicke, Y., Singh, N., et al. (2023). Exploring the MIT mathematics and EECS curriculum using language models. arXiv preprint large arXiv:2306.08997. | 2311.04915#23 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 23,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Wondra, J. D., & Ellsworth, P. C. (2015). An appraisal theory of empathy and other vicarious emotional experiences. Psychological review, 122(3), 411.\nWubbolding, R. E., Casstevens, W. J., & Fulkerson, M. H. (2017). Using the wdep system of reality therapy to support person- treatment planning. Journal of centered Counseling & Development, 95(4), 472â477.\nYao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601.\nZaki, J. (2019). The war for kindness: Building empathy in a fractured world. Crown.\nZhang, S. J., Florin, S., Lee, A. N., Niknafs, E., Marginean, A., Wang, A., Tyser, K., Chin, Z., Hicke, Y., Singh, N., et al. (2023). Exploring the MIT mathematics and EECS curriculum using language models. arXiv preprint large arXiv:2306.08997.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 24 | ð,ð¢ ð â¼ N z ð,ð¢ z ð ð,ð£ ð â¼ N ð,ð£ z ð , ðâ1 ð , ðâ1 ð , z . · Ið¾ · Ið¾ (2)
ð,ð¢ where ðð is the precision for the conditional prior of z . The ð horizontally-stacked matrices of vocab/collaborative/content token embeddings are denoted as Zð¡ , Zð,{ð¢,ð£ } , and Zð,{ð¢,ð£ } , respectively3.
3.2.3 CLLM4Rec Base Model. With user/item tokens and the corresponding token embeddings introduced in the previous sub- sections, we are ready to introduce the CLLM4Rec base model with expanded vocabulary. The CLLM4Rec base model is denoted with (ð¿) {ð,ð },1:ð = Ëððð {ð,ð } (x1:ð ), | 2311.01343#24 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 24,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "ð,ð¢ ð â¼ N z ð,ð¢ z ð ð,ð£ ð â¼ N ð,ð£ z ð , ðâ1 ð , ðâ1 ð , z . · Ið¾ · Ið¾ (2)\nð,ð¢ where ðð is the precision for the conditional prior of z . The ð horizontally-stacked matrices of vocab/collaborative/content token embeddings are denoted as Zð¡ , Zð,{ð¢,ð£ } , and Zð,{ð¢,ð£ } , respectively3.\n3.2.3 CLLM4Rec Base Model. With user/item tokens and the corresponding token embeddings introduced in the previous sub- sections, we are ready to introduce the CLLM4Rec base model with expanded vocabulary. The CLLM4Rec base model is denoted with (ð¿) {ð,ð },1:ð = Ëððð {ð,ð } (x1:ð ),",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 25 | (ð¿) which maps the token sequence x1:ð into the hidden space h {ð,ð },1:ð through ð¿ stacked self-attention module (the superscript (ð¿) will be omitted if no ambiguity exists); here, xð is a size ð + ð¼ + ð½ one-hot
3We use super/subscript ð and ð to distinguish the variables related to the collaborative and content model process, respectively.
Conferenceâ17, July 2017, Washington, DC, USA User!ID:0057 Item ID: 0046 Item Title: Wet n Wild Mega Last Lip Color 908C Sugar Plum Fairy Review: The color is a perfect mix of dark purple, red and pink. The only downside is the drying aspect of the lipstick, which I counteract by using lip balm before putting it on. filling as a the main collaborative effectiveness For interactions P and
# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1 | 2311.01343#25 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 25,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "(ð¿) which maps the token sequence x1:ð into the hidden space h {ð,ð },1:ð through ð¿ stacked self-attention module (the superscript (ð¿) will be omitted if no ambiguity exists); here, xð is a size ð + ð¼ + ð½ one-hot\n3We use super/subscript ð and ð to distinguish the variables related to the collaborative and content model process, respectively.\nConferenceâ17, July 2017, Washington, DC, USA User!ID:0057 Item ID: 0046 Item Title: Wet n Wild Mega Last Lip Color 908C Sugar Plum Fairy Review: The color is a perfect mix of dark purple, red and pink. The only downside is the drying aspect of the lipstick, which I counteract by using lip balm before putting it on. filling as a the main collaborative effectiveness For interactions P and\n# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 26 | # Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1
filling the pretexts in detail. Therefore, we can view the first part as a soft+hard prompt and conduct language modeling only on the main text. This encourages the model to focus exclusively on collaborative and content information, such that the stability and effectiveness of language modeling can be substantially enhanced. ð transformed from the historical interactions of user ð can be broken down into the soft+hard prompt ð,ð x ð
Figure 3: Example review data from Amazon Beauty dataset.
(a) Historical Interactions r;: soft+hard prompt x7? . rm item token seq. x | 2311.01343#26 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 26,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1\nfilling the pretexts in detail. Therefore, we can view the first part as a soft+hard prompt and conduct language modeling only on the main text. This encourages the model to focus exclusively on collaborative and content information, such that the stability and effectiveness of language modeling can be substantially enhanced. ð transformed from the historical interactions of user ð can be broken down into the soft+hard prompt ð,ð x ð\nFigure 3: Example review data from Amazon Beauty dataset.\n(a) Historical Interactions r;: soft+hard prompt x7? . rm item token seq. x",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 27 | Figure 3: Example review data from Amazon Beauty dataset.
(a) Historical Interactions r;: soft+hard prompt x7? . rm item token seq. x
vector denoting the token of either a vocab, a user, or an item. In addition, the subscript in Ëððð {ð,ð } denotes which embedding matrix is used to encode the user/item tokens (where ð stands for matrix Zð,{ð¢,ð£ } and ð stands for matrix Zð,{ð¢,ð£ } ). For the CLLM4Rec base Ëððð {ð,ð } , only the user/item token embeddings are trainable, model whereas the vocab embeddings Zð¡ as well as the other parts of the backbone LLM are fixed to preserve the pretrained knowledge. | 2311.01343#27 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 27,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Figure 3: Example review data from Amazon Beauty dataset.\n(a) Historical Interactions r;: soft+hard prompt x7? . rm item token seq. x\nvector denoting the token of either a vocab, a user, or an item. In addition, the subscript in Ëððð {ð,ð } denotes which embedding matrix is used to encode the user/item tokens (where ð stands for matrix Zð,{ð¢,ð£ } and ð stands for matrix Zð,{ð¢,ð£ } ). For the CLLM4Rec base Ëððð {ð,ð } , only the user/item token embeddings are trainable, model whereas the vocab embeddings Zð¡ as well as the other parts of the backbone LLM are fixed to preserve the pretrained knowledge.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 28 | Accordingly, we introduce the collaborative LLM by adding an item prediction head ðð : Rð¾â â P(ð½ ) to the CLLM4Rec base model Ëðððð , which maps the final-layer last-step hidden representation hð,â1 calculated via Ëðððð to the item probability space P(ð½ ) to predict the next item token. The weights of ðð are tied with the item collab- orative token embeddings Zð,ð£ as ðð (hð,â1) = softmax(Zð,𣠷 hð,â1). The generative process of the collaborative LLM can be denoted as:
# 3.3 Mutually-Regularized Pretraining | 2311.01343#28 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 28,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Accordingly, we introduce the collaborative LLM by adding an item prediction head ðð : Rð¾â â P(ð½ ) to the CLLM4Rec base model Ëðððð , which maps the final-layer last-step hidden representation hð,â1 calculated via Ëðððð to the item probability space P(ð½ ) to predict the next item token. The weights of ðð are tied with the item collab- orative token embeddings Zð,ð£ as ðð (hð,â1) = softmax(Zð,𣠷 hð,â1). The generative process of the collaborative LLM can be denoted as:\n# 3.3 Mutually-Regularized Pretraining",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 29 | # 3.3 Mutually-Regularized Pretraining
With CLLM4Rec base model introduced in the previous section, we discuss the mutually-regularized pretraining strategy for CLLM4Rec to learn the user/item collaborative/content token embeddings based on language modeling on corpora established from user- item interactions and user/item textual features, where the encoded knowledge and logical reasoning ability of the pretrained LLM can be fully utilized. The overall process can be referred to in Fig. 2.
rm of Pp Xi eel hr, (Xitel ie Xt ). (4)
ð,ð where the prompt x serves as a context to generate the next ð item token based on previous item tokens. Since the generation of ð,ð x ð,ð+1 requires attending to previous tokens, when maximizing the likelihood, the collaborative LLM pushes the token embeddings of ð,ð¢ user ð, i.e., z , and the token embeddings of the interacted items, i.e., ð ð,ð£ ð,ð£ ð , · · · , to be close to each other, where user/item collaborative z , z ð semantics in recommendation can be accurately captured. | 2311.01343#29 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 29,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 3.3 Mutually-Regularized Pretraining\nWith CLLM4Rec base model introduced in the previous section, we discuss the mutually-regularized pretraining strategy for CLLM4Rec to learn the user/item collaborative/content token embeddings based on language modeling on corpora established from user- item interactions and user/item textual features, where the encoded knowledge and logical reasoning ability of the pretrained LLM can be fully utilized. The overall process can be referred to in Fig. 2.\nrm of Pp Xi eel hr, (Xitel ie Xt ). (4)\nð,ð where the prompt x serves as a context to generate the next ð item token based on previous item tokens. Since the generation of ð,ð x ð,ð+1 requires attending to previous tokens, when maximizing the likelihood, the collaborative LLM pushes the token embeddings of ð,ð¢ user ð, i.e., z , and the token embeddings of the interacted items, i.e., ð ð,ð£ ð,ð£ ð , · · · , to be close to each other, where user/item collaborative z , z ð semantics in recommendation can be accurately captured.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 30 | 3.3.1 Recommendation-Specific Corpora. Generally, we can transform the interactions and user/item content features into doc- uments of user/item/vocab token sequences as follows:
# Raw Corpora Transformed from Recommendation Data
Similarly, for the documents transformed from the user/item ð¢ð£,ð content5, it can also naturally be split into a soft+hard prompt x ð ð and the main text x
(a) Historical Interactions rð : <user_ð> has interacted with <item_ð> <item_ð> ... (b) User/Item Textual Features xð¢ The biography of <user_ð> is: Main biography. The content of <item_ð> is: Main contents. <user_ð> writes the review for <item_ð> : Main reviews.
(b) User/Item Textual Features xij vocab seq. xiâ soft+hard prompt x,â | 2311.01343#30 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 30,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.3.1 Recommendation-Specific Corpora. Generally, we can transform the interactions and user/item content features into doc- uments of user/item/vocab token sequences as follows:\n# Raw Corpora Transformed from Recommendation Data\nSimilarly, for the documents transformed from the user/item ð¢ð£,ð content5, it can also naturally be split into a soft+hard prompt x ð ð and the main text x\n(a) Historical Interactions rð : <user_ð> has interacted with <item_ð> <item_ð> ... (b) User/Item Textual Features xð¢ The biography of <user_ð> is: Main biography. The content of <item_ð> is: Main contents. <user_ð> writes the review for <item_ð> : Main reviews.\n(b) User/Item Textual Features xij vocab seq. xiâ soft+hard prompt x,â",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 31 | (b) User/Item Textual Features xij vocab seq. xiâ soft+hard prompt x,â
Accordingly, we introduce the content LLM by adding a vocab prediction head ðð : Rð¾â â P(ð ) to the CLLM4Rec base model Ëðððð , which maps the final-layer last-step hidden representation hð,â1 calculated via Ëðððð (which shares the same pretrained LLM with Ëðððð but uses Zð,{ð¢,ð£ } to decode the user/item token) to the vocab probability space. Similarly, the weights of ðð are tied with the vocab embeddings Zð¡ as ðð (hð,â1) = softmax(Zð¡ · hð,â1). The generative process of the content LLM can be denoted as follows: | 2311.01343#31 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 31,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "(b) User/Item Textual Features xij vocab seq. xiâ soft+hard prompt x,â\nAccordingly, we introduce the content LLM by adding a vocab prediction head ðð : Rð¾â â P(ð ) to the CLLM4Rec base model Ëðððð , which maps the final-layer last-step hidden representation hð,â1 calculated via Ëðððð (which shares the same pretrained LLM with Ëðððð but uses Zð,{ð¢,ð£ } to decode the user/item token) to the vocab probability space. Similarly, the weights of ðð are tied with the vocab embeddings Zð¡ as ðð (hð,â1) = softmax(Zð¡ · hð,â1). The generative process of the content LLM can be denoted as follows:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 32 | where an example based on the Amazon Beauty dataset can be referred to in Fig. 3. However, directly conducting language model- ing on the raw corpora is clearly infeasible, as each document is composed of heterogeneous vocab, user, and item tokens, where the number of meaningful vocab tokens (e.g., â¼ 50k for GPT, and â¼ 30k for T5) can be diluted by the large number of newly introduced user/item tokens with randomly initialized embeddings.
3.3.2 Soft+Hard Prompting. To address the above challenge, we propose a novel soft+hard prompting strategy to facilitate language modeling on RS-specific corpora with heterogeneous user/item/vocab tokens. The strategy is based on a key observation that documents transformed from both user-item interactions rð and user/item tex- tual features xð¢ ð ð can be broken down into two parts: A heterogeneous part composed of soft (user/item) and hard (vocab) tokens providing context information regarding the gist of the doc- ument, and a main text part with homogeneous item/vocab tokens
cm fe uum jum ~ud,p Xie ~ itn, ( ijk ij, 1:k ⢠) () | 2311.01343#32 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 32,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "where an example based on the Amazon Beauty dataset can be referred to in Fig. 3. However, directly conducting language model- ing on the raw corpora is clearly infeasible, as each document is composed of heterogeneous vocab, user, and item tokens, where the number of meaningful vocab tokens (e.g., â¼ 50k for GPT, and â¼ 30k for T5) can be diluted by the large number of newly introduced user/item tokens with randomly initialized embeddings.\n3.3.2 Soft+Hard Prompting. To address the above challenge, we propose a novel soft+hard prompting strategy to facilitate language modeling on RS-specific corpora with heterogeneous user/item/vocab tokens. The strategy is based on a key observation that documents transformed from both user-item interactions rð and user/item tex- tual features xð¢ ð ð can be broken down into two parts: A heterogeneous part composed of soft (user/item) and hard (vocab) tokens providing context information regarding the gist of the doc- ument, and a main text part with homogeneous item/vocab tokens\ncm fe uum jum ~ud,p Xie ~ itn, ( ijk ij, 1:k ⢠) ()",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 33 | cm fe uum jum ~ud,p Xie ~ itn, ( ijk ij, 1:k ⢠) ()
ð¢ð£,ð ð ð,1:ð ð¢ð£,ð ð ð,ð+1 based on previously as the context.
which generates the next vocab token x ð¢ð£,ð ð¢ð£,ð ð ð,1:ð with prompt x ð ð generated vocab tokens x
4We use the superscripts ð and ð to distinguish the prompt and the main text. 5Hereafter, we take xð¢ð£ an example for discussions, which can be easily generalized to the case of xð¢
Collaborative Large Language Model for Recommender Systems | 2311.01343#33 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 33,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "cm fe uum jum ~ud,p Xie ~ itn, ( ijk ij, 1:k ⢠) ()\nð¢ð£,ð ð ð,1:ð ð¢ð£,ð ð ð,ð+1 based on previously as the context.\nwhich generates the next vocab token x ð¢ð£,ð ð¢ð£,ð ð ð,1:ð with prompt x ð ð generated vocab tokens x\n4We use the superscripts ð and ð to distinguish the prompt and the main text. 5Hereafter, we take xð¢ð£ an example for discussions, which can be easily generalized to the case of xð¢\nCollaborative Large Language Model for Recommender Systems",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 34 | Collaborative Large Language Model for Recommender Systems
When maximizing the likelihood, the content information in xð¢ð£,ð can be encoded in the content token embeddings of user ð and item ð,ð¢ ð, i.e., z , where the pretrained knowledge of the LLM can ð be fully utilized. For example, for the reviews shown in Fig. 3, the pretrained LLM will know that <item_46> is a lipstick with dark purple, red, and pink colors and can have side effects of drying lip, and reasons that <user_57> likes the colors but hates the side effects, which can be alleviated by the lip balm. | 2311.01343#34 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 34,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Collaborative Large Language Model for Recommender Systems\nWhen maximizing the likelihood, the content information in xð¢ð£,ð can be encoded in the content token embeddings of user ð and item ð,ð¢ ð, i.e., z , where the pretrained knowledge of the LLM can ð be fully utilized. For example, for the reviews shown in Fig. 3, the pretrained LLM will know that <item_46> is a lipstick with dark purple, red, and pink colors and can have side effects of drying lip, and reasons that <user_57> likes the colors but hates the side effects, which can be alleviated by the lip balm.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 35 | Discussion. Generally, since the "hard" (i.e., the vocab) part of ð,ð the prompts x is what the pretrained LLM could un- ð derstand, they are designed to trigger the reasoning ability of the pretrained LLM based on its encoded knowledge. For example, the ð,ð relational phrase "has interacted with" in the prompt x guides ð the collaborative LLM to understand that the newly-introduced ð,ð token <user_i> is a user subject and the tokens in the prompt x ð are the objects of interacted item sequences. Meanwhile, the con- ð¢ð£,ð texts "write the review for" in x direct the content LLM to ð ð , i.e., <user_ð>âs better understand the nature of main texts in x judgment on the <item_ð> based on the personal using experience. The specific formulation of the prompt can be flexible, as Geng et al. has demonstrated that the variation in the expression of the prompt makes less difference, as long as the meaning is the same and the prompt is consistent across the training and testing phases. | 2311.01343#35 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 35,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Discussion. Generally, since the \"hard\" (i.e., the vocab) part of ð,ð the prompts x is what the pretrained LLM could un- ð derstand, they are designed to trigger the reasoning ability of the pretrained LLM based on its encoded knowledge. For example, the ð,ð relational phrase \"has interacted with\" in the prompt x guides ð the collaborative LLM to understand that the newly-introduced ð,ð token <user_i> is a user subject and the tokens in the prompt x ð are the objects of interacted item sequences. Meanwhile, the con- ð¢ð£,ð texts \"write the review for\" in x direct the content LLM to ð ð , i.e., <user_ð>âs better understand the nature of main texts in x judgment on the <item_ð> based on the personal using experience. The specific formulation of the prompt can be flexible, as Geng et al. has demonstrated that the variation in the expression of the prompt makes less difference, as long as the meaning is the same and the prompt is consistent across the training and testing phases.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 36 | 3.3.3 Mutually-Regularization. Since the pretrained LLMs are not recommendation-oriented, naively optimizing the language modeling objective as Eq. (5) unavoidably captures noise irrele- vant to recommendations. In addition, since the user/item interac- tions are sparse, the collaborative LLM can easily overfit on the ob- served interactions. To address this issue, we propose the mutually- regularized pretraining for CLLM4Rec, where collaborative LLM can guide content LLM to capture recommendation-oriented in- formation from user/item content, and content LLM can in turn introduce side information to support collaborative filtering.
The mutual-regularization naturally arises with the generative process of the CLLM4Rec pretraining stage defined in the previous subsections. If we denote the stacked item token embeddings as ð,ð£ , which contains item ð and other items interacted by the Z ð user ð, the generation process of CLLM4Rec associated with xð ð and xð¢ð£ ð ð can be defined as the joint distribution as follows: | 2311.01343#36 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 36,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.3.3 Mutually-Regularization. Since the pretrained LLMs are not recommendation-oriented, naively optimizing the language modeling objective as Eq. (5) unavoidably captures noise irrele- vant to recommendations. In addition, since the user/item interac- tions are sparse, the collaborative LLM can easily overfit on the ob- served interactions. To address this issue, we propose the mutually- regularized pretraining for CLLM4Rec, where collaborative LLM can guide content LLM to capture recommendation-oriented in- formation from user/item content, and content LLM can in turn introduce side information to support collaborative filtering.\nThe mutual-regularization naturally arises with the generative process of the CLLM4Rec pretraining stage defined in the previous subsections. If we denote the stacked item token embeddings as ð,ð£ , which contains item ð and other items interacted by the Z ð user ð, the generation process of CLLM4Rec associated with xð ð and xð¢ð£ ð ð can be defined as the joint distribution as follows:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 37 | rm .uam Lu glo cu 9c0| 1p uop) _ p(x, Kip 8 Zy 27 2; Ix; Xi) = ti rm orm rp). fe uv,m|_uv,m uvp) TAP him, ck Pike) MP Size Pajakâv Xi LM for collab. LLM ull, 0} 1, Li 1 (2 lai") Te (25 leh?) -» (ai) - Tap (242) LM for content LLM mutual regularization prior
(6) A scrutiny of Eq. (6) reveals that the joint distribution can be decom- posed into three parts: 1) the language modeling of the collaborative and content LLMs that learn user/item token embeddings as Eqs. (4) and (5); 2) the mutual regularization that connects the user/item token embeddings of the two LLMs (i.e., according to Eqs. (1-2),
Conferenceâ17, July 2017, Washington, DC, USA | 2311.01343#37 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 37,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "rm .uam Lu glo cu 9c0| 1p uop) _ p(x, Kip 8 Zy 27 2; Ix; Xi) = ti rm orm rp). fe uv,m|_uv,m uvp) TAP him, ck Pike) MP Size Pajakâv Xi LM for collab. LLM ull, 0} 1, Li 1 (2 lai\") Te (25 leh?) -» (ai) - Tap (242) LM for content LLM mutual regularization prior\n(6) A scrutiny of Eq. (6) reveals that the joint distribution can be decom- posed into three parts: 1) the language modeling of the collaborative and content LLMs that learn user/item token embeddings as Eqs. (4) and (5); 2) the mutual regularization that connects the user/item token embeddings of the two LLMs (i.e., according to Eqs. (1-2),\nConferenceâ17, July 2017, Washington, DC, USA",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 38 | Conferenceâ17, July 2017, Washington, DC, USA
Pp (2",") and p (242¢) are conditional Gaussians, which will introduce MSE regularization between a ght, and z co Lo 12; Lik when ik? i log-likelihood is maximized) 3) the prior of gin and ai » which will be ignored due to the existence of mutual regularization (i.e., setting the precision A; in the prior in Eq. (1) as zero).
We use Maximum a Posteriori (MAP) to estimate the user/item ð,ð£ ð,ð¢ , Z token embeddings z , where the objective is pro- ð ð portional to the logarithm of the joint distribution specified in Eq. (4). We take alternative steps to optimize the MAP objective. If we denote the trainable parameters associated with the item token prediction head ðð and vocab token prediction head ðð as ð½ð (which are tied with the corresponding token embeddings), the objective for the collaborative LLM (L-step) and content LLM (C-step) with mutual regularization can be derived as follows: | 2311.01343#38 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 38,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Conferenceâ17, July 2017, Washington, DC, USA\nPp (2\"\u0002,\") and p (242¢) are conditional Gaussians, which will introduce MSE regularization between a ght, and z co Lo 12; Lik when ik? i log-likelihood is maximized) 3) the prior of gin and ai » which will be ignored due to the existence of mutual regularization (i.e., setting the precision A; in the prior in Eq. (1) as zero).\nWe use Maximum a Posteriori (MAP) to estimate the user/item ð,ð£ ð,ð¢ , Z token embeddings z , where the objective is pro- ð ð portional to the logarithm of the joint distribution specified in Eq. (4). We take alternative steps to optimize the MAP objective. If we denote the trainable parameters associated with the item token prediction head ðð and vocab token prediction head ðð as ð½ð (which are tied with the corresponding token embeddings), the objective for the collaborative LLM (L-step) and content LLM (C-step) with mutual regularization can be derived as follows:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 39 | L-step. In the L-step, we fix user/item content embeddings aan Vis as a, Via in Eq. (6), and use them to constrain the user/item collaborative embeddings along with the language modeling of collaborative LLM, leading to the following composite objective: MAP (,Lu jlo _ -> fi rm|orm np LY step (2; »Z; 6) = DP in, xe ke Xi ke
MAP (,Lu jlo _ -> fi rm|orm np LY step (2; »Z; 6) = DP in, xe ke Xi ke LM loss for collab. LLM Ae || Lu _ zeul|? Ac || bo gcoll® â Ar || tull Az | Le Bre -5 $a Fe -2B k MR loss with content LLM Prior loss
# ð,ð£ , Z ð
+ Cð ,
(7) where Cð is the constant irrelevant for optimization. The LM loss captures the collaborative similarity between token embeddings of user ð and the interacted items, where side information can be introduced via the MR loss to support collaborative filtering. | 2311.01343#39 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 39,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "L-step. In the L-step, we fix user/item content embeddings aan Vis as a, Via in Eq. (6), and use them to constrain the user/item collaborative embeddings along with the language modeling of collaborative LLM, leading to the following composite objective: MAP (,Lu jlo _ -> fi rm|orm np LY step (2; »Z; 6) = DP in, xe ke Xi ke\nMAP (,Lu jlo _ -> fi rm|orm np LY step (2; »Z; 6) = DP in, xe ke Xi ke LM loss for collab. LLM Ae || Lu _ zeul|? Ac || bo gcoll® â Ar || tull Az | Le Bre -5 $a Fe -2B k MR loss with content LLM Prior loss\n# ð,ð£ , Z ð\n+ Cð ,\n(7) where Cð is the constant irrelevant for optimization. The LM loss captures the collaborative similarity between token embeddings of user ð and the interacted items, where side information can be introduced via the MR loss to support collaborative filtering.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 40 | C-step. After one-step optimization of the L-step, we fix the user/item ð,ð¢ collaborative token embeddings z in Eq. (6), lead- ð ing to the following composite objective for the content LLM:
MAP [,c,u co a Te uv,m|_uo,m uv,p Le step (2; me] 8) = dep f; (xin PG jickâv % ) k Ime
LM loss for content LLM |Lao _ gholl* 4 0 J 200°
Ae Jou _ ghul? Ae 2% tlle J MR loss with collab. LLM
Ae Jou _ ghul? Ae |Lao _ gholl* 4 0 2% tlle J 200°
(8)
where MR loss constrains content LLM to capture recommendation- oriented information from user/item textual features. In Eqs. (7) and (8), ðð controls the strength of mutual regularization, which will be thoroughly discussed in the empirical study. | 2311.01343#40 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 40,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "C-step. After one-step optimization of the L-step, we fix the user/item ð,ð¢ collaborative token embeddings z in Eq. (6), lead- ð ing to the following composite objective for the content LLM:\nMAP [,c,u co a Te uv,m|_uo,m uv,p Le step (2; me] 8) = dep f; (xin PG jickâv % ) k Ime\nLM loss for content LLM |Lao _ gholl* 4 0 J 200°\nAe Jou _ ghul? Ae 2% tlle J MR loss with collab. LLM\nAe Jou _ ghul? Ae |Lao _ gholl* 4 0 2% tlle J 200°\n(8)\nwhere MR loss constrains content LLM to capture recommendation- oriented information from user/item textual features. In Eqs. (7) and (8), ðð controls the strength of mutual regularization, which will be thoroughly discussed in the empirical study.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 41 | 3.3.4 Stochastic Item Reordering. Another issue that hinders effective collaborative filtering via Eq. (7) is the order of item to- kens when transforming the historical interactions rð into a token ð,ð sequence x for language modeling. Item order usually does not ð matter for collaborative filtering (even if it matters, the positional embeddings denoting the order of natural language may not cap- ture the semantics of the order of interactions). To address this ð,ð issue, we propose to randomly permute the item tokens in x ð
Conferenceâ17, July 2017, Washington, DC, USA
ð,ð with prompt x ð fixed when optimizing the collaborative LLM as Eq. (7). Through this strategy, the order of interacted items can be ð,ð ignored without negative influence on the vocab tokens in x ð
# 3.4 Recommendation-Oriented Finetuning | 2311.01343#41 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 41,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.3.4 Stochastic Item Reordering. Another issue that hinders effective collaborative filtering via Eq. (7) is the order of item to- kens when transforming the historical interactions rð into a token ð,ð sequence x for language modeling. Item order usually does not ð matter for collaborative filtering (even if it matters, the positional embeddings denoting the order of natural language may not cap- ture the semantics of the order of interactions). To address this ð,ð issue, we propose to randomly permute the item tokens in x ð\nConferenceâ17, July 2017, Washington, DC, USA\nð,ð with prompt x ð fixed when optimizing the collaborative LLM as Eq. (7). Through this strategy, the order of interacted items can be ð,ð ignored without negative influence on the vocab tokens in x ð\n# 3.4 Recommendation-Oriented Finetuning",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 42 | # 3.4 Recommendation-Oriented Finetuning
3.4.1 Pretraining v.s. Finetuning. The pretraining of CLLM4Rec aims to learn user/item token embeddings based on the large cor- pus of documents transformed from user-item interactions rð and ð , xð¢ð£ user/item textual features xð¢ ð ð via language modeling. How- ever, for now, the pretrained CLLM4Rec can only complete item/vocab token sequences based on the soft+hard prompts, and therefore the gap between NLP and RS is still not completely eliminated. In addition, naively treating the collaborative LLM as a recom- mendation model can lead to huge computational costs where the recommended items are sequentially generated via auto-regression. Therefore, we propose a recommendation-oriented finetuning strat- egy for CLLM4Rec, which aims to finetune the pretrained collabo- rative LLM and tailor it for efficient recommendations. | 2311.01343#42 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 42,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 3.4 Recommendation-Oriented Finetuning\n3.4.1 Pretraining v.s. Finetuning. The pretraining of CLLM4Rec aims to learn user/item token embeddings based on the large cor- pus of documents transformed from user-item interactions rð and ð , xð¢ð£ user/item textual features xð¢ ð ð via language modeling. How- ever, for now, the pretrained CLLM4Rec can only complete item/vocab token sequences based on the soft+hard prompts, and therefore the gap between NLP and RS is still not completely eliminated. In addition, naively treating the collaborative LLM as a recom- mendation model can lead to huge computational costs where the recommended items are sequentially generated via auto-regression. Therefore, we propose a recommendation-oriented finetuning strat- egy for CLLM4Rec, which aims to finetune the pretrained collabo- rative LLM and tailor it for efficient recommendations.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 43 | 3.4.2 Masked Prompting with Multinomial Head. To achieve this purpose, we first design a masked prompting strategy to gen- erate recommendation-oriented prompts. For each user, we ran- domly mask the interacted items rð by 100 Ã ðð%, where the re- maining items are denoted as rððð ððð , and use it to generate a ð ððð,ð recommendation-oriented prompt x . All the hold-out items, ð which we denote with a multi-hot vector râððð , are treated as the ððð,ð target. The prompt x ð
(c) Recommendation Prompts & Target (prompt) <user_ð> has interacted with <item_ð â²> <item_ð â²> the user will interact with: (target) râððð | 2311.01343#43 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 43,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.4.2 Masked Prompting with Multinomial Head. To achieve this purpose, we first design a masked prompting strategy to gen- erate recommendation-oriented prompts. For each user, we ran- domly mask the interacted items rð by 100 Ã ðð%, where the re- maining items are denoted as rððð ððð , and use it to generate a ð ððð,ð recommendation-oriented prompt x . All the hold-out items, ð which we denote with a multi-hot vector râððð , are treated as the ððð,ð target. The prompt x ð\n(c) Recommendation Prompts & Target (prompt) <user_ð> has interacted with <item_ð â²> <item_ð â²> the user will interact with: (target) râððð",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 44 | which triggers the reasoning ability of the pretrained LLM by using relational phrase "has interacted with" to describe the historical interactions, and using the phrase "the user will interact with" to guide the prediction of the target items râððð
We name CLLM4Rec in the finetuning stage as RecLLM, which inherits the CLLM4Rec base model lim, from the collaborative LLM in the pretraining stage and introduces a new item prediction head with multinomial likelihood, ie., frec, whose weights are also tied with the item token embeddings Z!â. The generation of the hold hold-out items r/°"â via the RecLLM can be formulated as follows: rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5
rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5 (9) | 2311.01343#44 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 44,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "which triggers the reasoning ability of the pretrained LLM by using relational phrase \"has interacted with\" to describe the historical interactions, and using the phrase \"the user will interact with\" to guide the prediction of the target items râððð\nWe name CLLM4Rec in the finetuning stage as RecLLM, which inherits the CLLM4Rec base model lim, from the collaborative LLM in the pretraining stage and introduces a new item prediction head with multinomial likelihood, ie., frec, whose weights are also tied with the item token embeddings Z!â. The generation of the hold hold-out items r/°\"â via the RecLLM can be formulated as follows: rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5\nrhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5 (9)",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 45 | rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5 (9)
(9) where ðð¢ðð¡ð denotes the multinomial distribution and ð âððð is the number of hold-out items for user ð. When finetuning the RecLLM according to Eq. (9), hððð ð,ð,â1, which can be viewed as the user la- tent variable summarizing the historical interaction of user ð, is encouraged to be similar to the collaborative embeddings of all the interacted items. In addition, we keep it regularized with the content LLM in a similar manner as Eq. (7), and use the stochastic ððð,ð 6. Through item reordering strategy to generate the prompt x ð the proposed finetuning strategy, CLLM4Rec can fully utilize the encoded knowledge from the pretrained LLM backbone and the
6The objective of the RecLLM is formulated in Eq. (10) in Appendix A.2. | 2311.01343#45 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 45,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5 (9)\n(9) where ðð¢ðð¡ð denotes the multinomial distribution and ð âððð is the number of hold-out items for user ð. When finetuning the RecLLM according to Eq. (9), hððð ð,ð,â1, which can be viewed as the user la- tent variable summarizing the historical interaction of user ð, is encouraged to be similar to the collaborative embeddings of all the interacted items. In addition, we keep it regularized with the content LLM in a similar manner as Eq. (7), and use the stochastic ððð,ð 6. Through item reordering strategy to generate the prompt x ð the proposed finetuning strategy, CLLM4Rec can fully utilize the encoded knowledge from the pretrained LLM backbone and the\n6The objective of the RecLLM is formulated in Eq. (10) in Appendix A.2.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 47 | # 3.5 Predictions with CLLM4Rec
After the pretraining and finetuning of CLLM4Rec, to make recom- mendation for user ð, we can convert the whole historical interac- tions of the user, i.e., rð , into the recommendation-oriented prompt ððð,ð Ëx as described in Section 3.4.2 (with no masked items) and input ð it into the RecLLM model. Then, the multinomial probability Ërð over all ð½ items can be obtained through one forward propagation via = Ëðððð Ërð = ðð¢ðð¡ð , where uninteracted items with top-ð scores in Ërð can be selected as recommendations.
# 4 EMPIRICAL STUDY
In this section, we present the experiments on four public datasets and one LinkedIn dataset to demonstrate the effectiveness of CLLM4Rec, aiming to answer the following research questions. | 2311.01343#47 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 47,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 3.5 Predictions with CLLM4Rec\nAfter the pretraining and finetuning of CLLM4Rec, to make recom- mendation for user ð, we can convert the whole historical interac- tions of the user, i.e., rð , into the recommendation-oriented prompt ððð,ð Ëx as described in Section 3.4.2 (with no masked items) and input ð it into the RecLLM model. Then, the multinomial probability Ërð over all ð½ items can be obtained through one forward propagation via = Ëðððð Ërð = ðð¢ðð¡ð , where uninteracted items with top-ð scores in Ërð can be selected as recommendations.\n# 4 EMPIRICAL STUDY\nIn this section, we present the experiments on four public datasets and one LinkedIn dataset to demonstrate the effectiveness of CLLM4Rec, aiming to answer the following research questions.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 48 | In this section, we present the experiments on four public datasets and one LinkedIn dataset to demonstrate the effectiveness of CLLM4Rec, aiming to answer the following research questions.
RQ1. How does CLLM4Rec, the first RS that tightly couples the ID-based paradigm with the LLM-based paradigm, perform compared to state-of-the-art ID-based and LLM-based RSs? ⢠RQ2. How does the pretraining stage of CLLM4Rec (including the mutual regularization trick and the stochastic item reorder strategy) influence the performance of CLLM4Rec?
⢠RQ3. How does the finetuning stage of CLLM4Rec with masked prompt and multinomial item prediction head influence the efficiency and effectiveness of recommendations.
# 4.1 Experimental Setup | 2311.01343#48 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 48,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "In this section, we present the experiments on four public datasets and one LinkedIn dataset to demonstrate the effectiveness of CLLM4Rec, aiming to answer the following research questions.\nRQ1. How does CLLM4Rec, the first RS that tightly couples the ID-based paradigm with the LLM-based paradigm, perform compared to state-of-the-art ID-based and LLM-based RSs? ⢠RQ2. How does the pretraining stage of CLLM4Rec (including the mutual regularization trick and the stochastic item reorder strategy) influence the performance of CLLM4Rec?\n⢠RQ3. How does the finetuning stage of CLLM4Rec with masked prompt and multinomial item prediction head influence the efficiency and effectiveness of recommendations.\n# 4.1 Experimental Setup",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 49 | # 4.1 Experimental Setup
4.1.1 Datasets. The experiments are mainly based on four pub- lic datasets: Amazon (AM)-Beauty dataset, AM-Toys dataset, AM- Sports dataset [17] and the Yelp dataset [38], where we binarize the interactions by keeping only ratings > 3 and treat them as implicit feedback [39]. In addition, we filter the dataset such that they keep the original 5-core property after binarization. For each user, we randomly select 80% of interactions for training, 10% for validation, and 10% for testing, where as least one item is selected in the valida- tion and the test set. The reviews that users provide to the items are collected as the textual feature xð¢ð£ ð ð . The real-world experiments are based on a job recommendation dataset collected nearline at the Company, where userâs click on the job Ads are logged as the implicit feedback, and usersâ self-provided biography xð¢ ð and the job descriptions xð£ ð are collected as the textual features, respectively. The statistics of the dataset are summarized in Table 3 in Appendix. | 2311.01343#49 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 49,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 4.1 Experimental Setup\n4.1.1 Datasets. The experiments are mainly based on four pub- lic datasets: Amazon (AM)-Beauty dataset, AM-Toys dataset, AM- Sports dataset [17] and the Yelp dataset [38], where we binarize the interactions by keeping only ratings > 3 and treat them as implicit feedback [39]. In addition, we filter the dataset such that they keep the original 5-core property after binarization. For each user, we randomly select 80% of interactions for training, 10% for validation, and 10% for testing, where as least one item is selected in the valida- tion and the test set. The reviews that users provide to the items are collected as the textual feature xð¢ð£ ð ð . The real-world experiments are based on a job recommendation dataset collected nearline at the Company, where userâs click on the job Ads are logged as the implicit feedback, and usersâ self-provided biography xð¢ ð and the job descriptions xð£ ð are collected as the textual features, respectively. The statistics of the dataset are summarized in Table 3 in Appendix.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 50 | Implementation Details. Due to the space limitation, we 4.1.2 only discuss CLLM4Rec with GPT-2 backbone with token embed- ding 768 and token size 50,257 in this section, where experiments with T5 backbone are discussed in Appendix B. During the train- ing stage, we first optimize the content LLM as Eq. (5) via lan- guage modeling for 10 epochs to warm up the user/item content token embeddings. Then, in the mutually-regularized pretraining stage, we alternatively train the collaborative and content LLMs as specified in Eqs. (7) and (8) for 100 epochs. Finally, we conduct the recommendation-oriented finetuning for 150 epochs, where the RecLLM is monitored with metrics Recall@20, Recall@40, and
Collaborative Large Language Model for Recommender Systems
NDCG@100 calculated on the validation set as with [39]. RecLLM with the best performance are logged and evaluated on the test set as the final results. ðð in Eqs. (7) and (8) is an important hyper- parameter, we first fix its value to the optimal one found by grid search, and then discuss its influence in Section 4.3.
# 4.2 Comparison with Baselines | 2311.01343#50 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 50,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Implementation Details. Due to the space limitation, we 4.1.2 only discuss CLLM4Rec with GPT-2 backbone with token embed- ding 768 and token size 50,257 in this section, where experiments with T5 backbone are discussed in Appendix B. During the train- ing stage, we first optimize the content LLM as Eq. (5) via lan- guage modeling for 10 epochs to warm up the user/item content token embeddings. Then, in the mutually-regularized pretraining stage, we alternatively train the collaborative and content LLMs as specified in Eqs. (7) and (8) for 100 epochs. Finally, we conduct the recommendation-oriented finetuning for 150 epochs, where the RecLLM is monitored with metrics Recall@20, Recall@40, and\nCollaborative Large Language Model for Recommender Systems\nNDCG@100 calculated on the validation set as with [39]. RecLLM with the best performance are logged and evaluated on the test set as the final results. ðð in Eqs. (7) and (8) is an important hyper- parameter, we first fix its value to the optimal one found by grid search, and then discuss its influence in Section 4.3.\n# 4.2 Comparison with Baselines",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 51 | # 4.2 Comparison with Baselines
4.2.1 Baselines. To demonstrate the multifaceted superiority of the proposed CLLM4Rec, we include the following ID-based and (L)LM-based RSs as the baselines for comparisons:
# ID-based Baselines.
Multi-Vae [39] is an ID-based collaborative filtering baseline that recommends new items by reconstructing the ratings rð via a variational auto-encoder (VAE) with multinomial likelihood. ⢠Md-Cvae [40] is a hybrid RS that extends the Multi-VAE by ð ð to reguintroducing a dual feature VAE on textual features xð¢ð£ larize the reconstruction of rð in the Multi-VAE.
# LM-based Baselines7.
⢠Bert4Rec [41] uses masked language modeling (MLM) pro- posed in BERT [32] to learn user/item embeddings for recom- mendation with bidirectional self-attention mechanism.
⢠S3Rec [38] extends BERT4Rec by augmenting the MLM with auxiliary tasks such as item attribute prediction, where content features can be fused for self-supervised learning.
# LLM-based Baselines. (a) Qualitative Analysis. | 2311.01343#51 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 51,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 4.2 Comparison with Baselines\n4.2.1 Baselines. To demonstrate the multifaceted superiority of the proposed CLLM4Rec, we include the following ID-based and (L)LM-based RSs as the baselines for comparisons:\n# ID-based Baselines.\nMulti-Vae [39] is an ID-based collaborative filtering baseline that recommends new items by reconstructing the ratings rð via a variational auto-encoder (VAE) with multinomial likelihood. ⢠Md-Cvae [40] is a hybrid RS that extends the Multi-VAE by ð ð to reguintroducing a dual feature VAE on textual features xð¢ð£ larize the reconstruction of rð in the Multi-VAE.\n# LM-based Baselines7.\n⢠Bert4Rec [41] uses masked language modeling (MLM) pro- posed in BERT [32] to learn user/item embeddings for recom- mendation with bidirectional self-attention mechanism.\n⢠S3Rec [38] extends BERT4Rec by augmenting the MLM with auxiliary tasks such as item attribute prediction, where content features can be fused for self-supervised learning.\n# LLM-based Baselines. (a) Qualitative Analysis.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 52 | # LLM-based Baselines. (a) Qualitative Analysis.
Both pseudo-ID-based and description-based methods discussed in Section 2.2 represent user/item with multiple tokens and formu- late direct recommendation as a token generation problem. Since the generated tokens could be irrelevant to the recommendation purpose, candidate items usually need to be explicitly provided in the prompt (e.g., P5 [20] provides 100 candidate items where one is positive, and TALLRec [36] outputs yes/no decision based on user/item descriptions in the prompts, etc.). In contrast, CLLM4Rec can generate multiple recommendations from the entire candidate pool. Therefore, these methods cannot directly work in our setting, and the comparisons are mainly based on qualitative analysis. (b) Quantitative Analysis
In addition, we design the following LLM-based baselines to
quantitatively demonstrate the effectiveness of CLLM4Rec. ⢠Llm-Scratch has the same structure as CLLM4Rec, but it trains the whole model from scratch instead of loading and fixing the weights of the pretrained LLM backbone.
⢠Llm-CF eliminates the content LLM from CLLM4Rec and the mutually-regularized pretraining step and uses only the collabo- rative LLM and RecLLM for recommendation. | 2311.01343#52 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 52,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# LLM-based Baselines. (a) Qualitative Analysis.\nBoth pseudo-ID-based and description-based methods discussed in Section 2.2 represent user/item with multiple tokens and formu- late direct recommendation as a token generation problem. Since the generated tokens could be irrelevant to the recommendation purpose, candidate items usually need to be explicitly provided in the prompt (e.g., P5 [20] provides 100 candidate items where one is positive, and TALLRec [36] outputs yes/no decision based on user/item descriptions in the prompts, etc.). In contrast, CLLM4Rec can generate multiple recommendations from the entire candidate pool. Therefore, these methods cannot directly work in our setting, and the comparisons are mainly based on qualitative analysis. (b) Quantitative Analysis\nIn addition, we design the following LLM-based baselines to\nquantitatively demonstrate the effectiveness of CLLM4Rec. ⢠Llm-Scratch has the same structure as CLLM4Rec, but it trains the whole model from scratch instead of loading and fixing the weights of the pretrained LLM backbone.\n⢠Llm-CF eliminates the content LLM from CLLM4Rec and the mutually-regularized pretraining step and uses only the collabo- rative LLM and RecLLM for recommendation.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 53 | ⢠Llm-FTALL has the same structure as CLLM4Rec, but it fine- tunes the whole network including the vocab embeddings as well as other parts of the pretrained LLM, instead of training only the newly-introduced user/item token embeddings.
7Note that both Bert4Rec and S3Rec are original designed for sequential recommenda- tion. In this paper, we use similar recommendation-oriented finetuning as CLLM4Rec to adapt them to direct recommendation, where item sequences generated from masked interactions are used to predict all hold-out items with multinomial likelihood.
Conferenceâ17, July 2017, Washington, DC, USA
# Table 1: Comparison between CLLM4Rec and various base- lines with GPT-backbone on three Amazon Review datasets. | 2311.01343#53 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 53,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "⢠Llm-FTALL has the same structure as CLLM4Rec, but it fine- tunes the whole network including the vocab embeddings as well as other parts of the pretrained LLM, instead of training only the newly-introduced user/item token embeddings.\n7Note that both Bert4Rec and S3Rec are original designed for sequential recommenda- tion. In this paper, we use similar recommendation-oriented finetuning as CLLM4Rec to adapt them to direct recommendation, where item sequences generated from masked interactions are used to predict all hold-out items with multinomial likelihood.\nConferenceâ17, July 2017, Washington, DC, USA\n# Table 1: Comparison between CLLM4Rec and various base- lines with GPT-backbone on three Amazon Review datasets.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 54 | AM-Beauty Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1295 0.1472 0.1126 0.1354 0.1720 0.2058 0.1677 0.1789 0.0835 0.0976 0.0781 0.0867 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0840 0.1319 0.1335 0.1524 0.1547 0.1265 0.1841 0.1988 0.2219 0.2196 0.0583 0.0855 0.0836 0.1072 0.1051 CLLM4Rec 0.1656 0.2323 0.1118 AM-Toys Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1076 0.1291 0.0853 0.1064 0.1558 0.1804 0.1375 0.1524 0.0781 0.0844 0.0532 0.0665 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0485 0.1027 0.1162 | 2311.01343#54 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 54,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "AM-Beauty Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1295 0.1472 0.1126 0.1354 0.1720 0.2058 0.1677 0.1789 0.0835 0.0976 0.0781 0.0867 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0840 0.1319 0.1335 0.1524 0.1547 0.1265 0.1841 0.1988 0.2219 0.2196 0.0583 0.0855 0.0836 0.1072 0.1051 CLLM4Rec 0.1656 0.2323 0.1118 AM-Toys Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1076 0.1291 0.0853 0.1064 0.1558 0.1804 0.1375 0.1524 0.0781 0.0844 0.0532 0.0665 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0485 0.1027 0.1162",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 55 | 0.0665 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0485 0.1027 0.1162 0.1342 0.1308 0.0771 0.1434 0.1542 0.1887 0.1859 0.0362 0.0680 0.0696 0.0889 0.0874 CLLM4Rec 0.1436 0.1933 0.0918 AM-Sports Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0659 0.0714 0.0521 0.0616 0.0975 0.1180 0.0701 0.0813 0.0446 0.0514 0.0305 0.0438 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0362 0.0642 0.0794 0.0901 0.0839 0.0538 0.0966 0.1002 0.1295 0.1248 0.0362 0.0419 0.0424 0.0592 0.0561 CLLM4Rec 0.0926 0.1351 0.0634 | 2311.01343#55 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 55,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "0.0665 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0485 0.1027 0.1162 0.1342 0.1308 0.0771 0.1434 0.1542 0.1887 0.1859 0.0362 0.0680 0.0696 0.0889 0.0874 CLLM4Rec 0.1436 0.1933 0.0918 AM-Sports Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0659 0.0714 0.0521 0.0616 0.0975 0.1180 0.0701 0.0813 0.0446 0.0514 0.0305 0.0438 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0362 0.0642 0.0794 0.0901 0.0839 0.0538 0.0966 0.1002 0.1295 0.1248 0.0362 0.0419 0.0424 0.0592 0.0561 CLLM4Rec 0.0926 0.1351 0.0634",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 56 | ⢠Llm-FixOrd has the same structure as CLLM4Rec but it removes the stochastic item reordering strategy for both the collaborative LLM in pretraining and the RecLLM in finetuning.
⢠Llm-PreRec discards finetuning and ranks the categorical prob- ability from the next item token prediction head of the collabora- tive LLM in the pretraining stage to make recommendations.
4.2.2 Results on the Public Datasets. We first analyze the ex- perimental results on four public datasets to provide preliminary answers for RQs. 1, 2, 3. From Tables 1 and 2, we can find that the ID-base method, Multi-VAE, remains a strong baseline for col- laborative filtering (CF). LLM-CF, the CF backbone of CLLM4Rec, cannot beat Multi-VAE on both AM-Sports and Toys datasets, even if the "hard" part of the prompt triggers the reasoning ability of the pretrained LLM. However, when large textual data are avail- able, CLLM4Rec outperforms its ID-based counterpart, MD-CVAE (which tightly couples an item content VAE with the Multi-VAE)
Conferenceâ17, July 2017, Washington, DC, USA
Table 2: Comparison between CLLM4Rec and various base- lines on the Yelp dataset and the Company dataset. | 2311.01343#56 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 56,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "⢠Llm-FixOrd has the same structure as CLLM4Rec but it removes the stochastic item reordering strategy for both the collaborative LLM in pretraining and the RecLLM in finetuning.\n⢠Llm-PreRec discards finetuning and ranks the categorical prob- ability from the next item token prediction head of the collabora- tive LLM in the pretraining stage to make recommendations.\n4.2.2 Results on the Public Datasets. We first analyze the ex- perimental results on four public datasets to provide preliminary answers for RQs. 1, 2, 3. From Tables 1 and 2, we can find that the ID-base method, Multi-VAE, remains a strong baseline for col- laborative filtering (CF). LLM-CF, the CF backbone of CLLM4Rec, cannot beat Multi-VAE on both AM-Sports and Toys datasets, even if the \"hard\" part of the prompt triggers the reasoning ability of the pretrained LLM. However, when large textual data are avail- able, CLLM4Rec outperforms its ID-based counterpart, MD-CVAE (which tightly couples an item content VAE with the Multi-VAE)\nConferenceâ17, July 2017, Washington, DC, USA\nTable 2: Comparison between CLLM4Rec and various base- lines on the Yelp dataset and the Company dataset.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 57 | Conferenceâ17, July 2017, Washington, DC, USA
Table 2: Comparison between CLLM4Rec and various base- lines on the Yelp dataset and the Company dataset.
Yelp Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0526 0.0664 0.0418 0.0563 0.0842 0.1058 0.0724 0.0893 0.0424 0.0497 0.0361 0.0485 LLM-Scratch LLM-CF LLM-FTAll LLM-FixOrd LLM-PreRec 0.0199 0.0541 0.0653 0.0694 0.0639 0.0325 0.0860 0.0989 0.1053 0.1021 0.0159 0.0412 0.0520 0.0524 0.0498 CLLM4Rec 0.0735 0.1149 0.0536 LinkedIn Recall@10 Recall@20 NDCG@10 Two-Tower 0.1186 0.2041 0.0979 M6-Retrieval CLLM4Rec-Emb CLLM4Rec 0.1279 0.1302 0.1427 0.2118 0.2165 0.2398 0.1020 0.1034 0.1199 | 2311.01343#57 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 57,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Conferenceâ17, July 2017, Washington, DC, USA\nTable 2: Comparison between CLLM4Rec and various base- lines on the Yelp dataset and the Company dataset.\nYelp Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0526 0.0664 0.0418 0.0563 0.0842 0.1058 0.0724 0.0893 0.0424 0.0497 0.0361 0.0485 LLM-Scratch LLM-CF LLM-FTAll LLM-FixOrd LLM-PreRec 0.0199 0.0541 0.0653 0.0694 0.0639 0.0325 0.0860 0.0989 0.1053 0.1021 0.0159 0.0412 0.0520 0.0524 0.0498 CLLM4Rec 0.0735 0.1149 0.0536 LinkedIn Recall@10 Recall@20 NDCG@10 Two-Tower 0.1186 0.2041 0.0979 M6-Retrieval CLLM4Rec-Emb CLLM4Rec 0.1279 0.1302 0.1427 0.2118 0.2165 0.2398 0.1020 0.1034 0.1199",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 58 | by a large margin. This is because MD-CVAE uses shallow bag- of-words to represent the textual features, for which pretrained LLMs in CLLM4Rec can provide deeper understanding via their pretrained knowledge. The importance of pretrained knowledge can also be shown by the LLM-Scratch model, which performs the worst among all included baselines. An interesting finding is that, LLM-FTAll, which finetunes the whole model including the pretrained LLM backbone, performs worse than CLLM4Rec, which optimizes only the newly introduced user/item token embeddings. The reason could be that, since the weights of the pretrained LLM are fully optimized, the recommendation-specific corpus is still not enough to adapt the pretrained LLM with good generalization ability for RS. Therefore, the cons of degenerating the pretrained knowledge outweigh the introduction of RS-specific knowledge. We can also find that LLM-PreRec, which uses the collaborative LLM in the pretraining stage to generate recommendations,is already a strong baseline. This demonstrates the effectiveness of the soft+hard prompting strategy, which facilitates efficient and stable language modeling on recommendation-oriented corpus with heterogeneous tokens. Still, CLLM4Rec performs better than LLM-PreRec, which shows the effectiveness of recommendation-oriented finetuning in adapting collaborative LLM for efficient recommendations. | 2311.01343#58 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 58,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "by a large margin. This is because MD-CVAE uses shallow bag- of-words to represent the textual features, for which pretrained LLMs in CLLM4Rec can provide deeper understanding via their pretrained knowledge. The importance of pretrained knowledge can also be shown by the LLM-Scratch model, which performs the worst among all included baselines. An interesting finding is that, LLM-FTAll, which finetunes the whole model including the pretrained LLM backbone, performs worse than CLLM4Rec, which optimizes only the newly introduced user/item token embeddings. The reason could be that, since the weights of the pretrained LLM are fully optimized, the recommendation-specific corpus is still not enough to adapt the pretrained LLM with good generalization ability for RS. Therefore, the cons of degenerating the pretrained knowledge outweigh the introduction of RS-specific knowledge. We can also find that LLM-PreRec, which uses the collaborative LLM in the pretraining stage to generate recommendations,is already a strong baseline. This demonstrates the effectiveness of the soft+hard prompting strategy, which facilitates efficient and stable language modeling on recommendation-oriented corpus with heterogeneous tokens. Still, CLLM4Rec performs better than LLM-PreRec, which shows the effectiveness of recommendation-oriented finetuning in adapting collaborative LLM for efficient recommendations.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 59 | 4.2.3 Results on the Company Dataset. In the real-world exper- iments, we compare CLLM4Rec with the two-tower (TT) model uti- lized in the Company for job recommendations. The TT model is im- plemented as a two-branch multi-layer perceptron (MLP), where the input user/item embeddings include embeddings extracted from a graph neural network (GNN) learned on user-job bipartite graph, as well as features extracted from an internal BERT model. In addition, since the textual features are available for almost every user and item, we compare CLLM4Rec with the state-of-the-art LLM-based RS, M6-Retrieval [19], which takes the dimensional-reduced last- layer embeddings of user/item descriptions from M6 Transformer for contrastive recommendations. The results are summarized in Table 2. For Table 2, we can find that CLLM4Rec outperforms the
# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1
(a) AM-Beauty Dataset (b) AM-Toys Dataset (c) AM-Sports Dataset (d) Yelp Dataset | 2311.01343#59 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 59,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "4.2.3 Results on the Company Dataset. In the real-world exper- iments, we compare CLLM4Rec with the two-tower (TT) model uti- lized in the Company for job recommendations. The TT model is im- plemented as a two-branch multi-layer perceptron (MLP), where the input user/item embeddings include embeddings extracted from a graph neural network (GNN) learned on user-job bipartite graph, as well as features extracted from an internal BERT model. In addition, since the textual features are available for almost every user and item, we compare CLLM4Rec with the state-of-the-art LLM-based RS, M6-Retrieval [19], which takes the dimensional-reduced last- layer embeddings of user/item descriptions from M6 Transformer for contrastive recommendations. The results are summarized in Table 2. For Table 2, we can find that CLLM4Rec outperforms the\n# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1\n(a) AM-Beauty Dataset (b) AM-Toys Dataset (c) AM-Sports Dataset (d) Yelp Dataset",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 60 | (a) AM-Beauty Dataset (b) AM-Toys Dataset (c) AM-Sports Dataset (d) Yelp Dataset
Figure 4: Sensitivity analysis w.r.t. ðð , which controls the strength of mutual-regularization for CLLM4Rec.
shallow TT model by a large margin. However, although the in- ference latency for CLLM4Rec is significantly improved compared with existing methods due to the introduction of recommendation- oriented finetuning, directly deploying CLLM4Rec online is still infeasible, as the inference budgets are higher compared to the TT model. Therefore, we design the CLLM4Rec-Emb baseline, which includes the user/item token embeddings Zð,ð¢ and Zð,ð£ learned from CLLM4Rec (projected into 128 dimensions) as extra inputs for the TT model, which demonstrates a performance improvement than the original TT model and the M6-Retrieval model in our offline ex- periment. This demonstrates the potential application of CLLM4Rec in industrial applications where low latency matters. | 2311.01343#60 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 60,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "(a) AM-Beauty Dataset (b) AM-Toys Dataset (c) AM-Sports Dataset (d) Yelp Dataset \nFigure 4: Sensitivity analysis w.r.t. ðð , which controls the strength of mutual-regularization for CLLM4Rec.\nshallow TT model by a large margin. However, although the in- ference latency for CLLM4Rec is significantly improved compared with existing methods due to the introduction of recommendation- oriented finetuning, directly deploying CLLM4Rec online is still infeasible, as the inference budgets are higher compared to the TT model. Therefore, we design the CLLM4Rec-Emb baseline, which includes the user/item token embeddings Zð,ð¢ and Zð,ð£ learned from CLLM4Rec (projected into 128 dimensions) as extra inputs for the TT model, which demonstrates a performance improvement than the original TT model and the M6-Retrieval model in our offline ex- periment. This demonstrates the potential application of CLLM4Rec in industrial applications where low latency matters.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 61 | 4.3 Parameter Sensitivity Analysis To further answer RQs. 2 and 3, we vary ðð in Eqs. (7), (8), and (10) that controls the strength of mutual regularization and investigates how it influences the performance of CLLM4Rec. From Fig. 4, we can find that, when ðð is small, the mutual regularization is weak, and content LLM cannot provide enough user/item content side in- formation to support the collaborative LLM and RecLLM. Therefore, the recommendation performance degenerates to a similar level as the LLM-CF. On the other hand, when ðð is too large, the MR loss in Eqs. (7), (8) and (10) dominates, which hinders CLLM4Rec from learning user/item token embeddings via language modeling and finetuning. Generally, for all four datasets, the performance of CLLM4Rec peaks at around ðð = 1, which serves as a good start when applying the GPT-based CLLM4Rec to new datasets.
# 5 CONCLUSION | 2311.01343#61 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 61,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "4.3 Parameter Sensitivity Analysis To further answer RQs. 2 and 3, we vary ðð in Eqs. (7), (8), and (10) that controls the strength of mutual regularization and investigates how it influences the performance of CLLM4Rec. From Fig. 4, we can find that, when ðð is small, the mutual regularization is weak, and content LLM cannot provide enough user/item content side in- formation to support the collaborative LLM and RecLLM. Therefore, the recommendation performance degenerates to a similar level as the LLM-CF. On the other hand, when ðð is too large, the MR loss in Eqs. (7), (8) and (10) dominates, which hinders CLLM4Rec from learning user/item token embeddings via language modeling and finetuning. Generally, for all four datasets, the performance of CLLM4Rec peaks at around ðð = 1, which serves as a good start when applying the GPT-based CLLM4Rec to new datasets.\n# 5 CONCLUSION",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 62 | # 5 CONCLUSION
In this paper, we proposed CLLM4Rec, the first method that tightly couples the ID paradigm and the LLM paradigm of RS, which faith- fully captures user/item semantics while fully utilizing encoded knowledge and logical reasoning ability of pretrained LLMs simul- taneously. Specifically, with mutually-regularized pretraining based on soft+hard prompting strategy, CLLM4Rec can effectively capture the user/item collaborative and content information via language modeling. Furthermore, with recommendation-oriented finetuning, the pretrained knowledge of CLLM4Rec can be fully utilized to efficiently generate recommendations. Extensive experiments show the multi-faceted superiority of CLLM4Rec over state-of-the-art.
Collaborative Large Language Model for Recommender Systems
REFERENCES [1] Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. Recommender Systems: An Introduction. Cambridge University Press, 2010. [2] James Bennett, Stan Lanning, et al. The Netflix prize. In KDD CUP, volume 2007,
page 35, 2007. | 2311.01343#62 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 62,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 5 CONCLUSION\nIn this paper, we proposed CLLM4Rec, the first method that tightly couples the ID paradigm and the LLM paradigm of RS, which faith- fully captures user/item semantics while fully utilizing encoded knowledge and logical reasoning ability of pretrained LLMs simul- taneously. Specifically, with mutually-regularized pretraining based on soft+hard prompting strategy, CLLM4Rec can effectively capture the user/item collaborative and content information via language modeling. Furthermore, with recommendation-oriented finetuning, the pretrained knowledge of CLLM4Rec can be fully utilized to efficiently generate recommendations. Extensive experiments show the multi-faceted superiority of CLLM4Rec over state-of-the-art.\nCollaborative Large Language Model for Recommender Systems\nREFERENCES [1] Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. Recommender Systems: An Introduction. Cambridge University Press, 2010. [2] James Bennett, Stan Lanning, et al. The Netflix prize. In KDD CUP, volume 2007,\npage 35, 2007.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 63 | page 35, 2007.
[3] Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. Where to go next for recommender systems? ID vs. modality- based recommender models revisited. arXiv preprint arXiv:2303.13835, 2023. [4] Andriy Mnih and Russ R Salakhutdinov. Probabilistic matrix factorization. In
NeurIPS, volume 20, 2007.
[5] Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, and Jason Weston. Starspace: Embed all the things! In AAAI, volume 32, 2018.
[6] Yehuda Koren, Steffen Rendle, and Robert Bell. Advances in collaborative filtering. Recommender systems handbook, pages 91â142, 2021.
[7] Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. Content-based rec- ommender systems: State of the art and trends. Recommender systems handbook, pages 73â105, 2011. | 2311.01343#63 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 63,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "page 35, 2007.\n[3] Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. Where to go next for recommender systems? ID vs. modality- based recommender models revisited. arXiv preprint arXiv:2303.13835, 2023. [4] Andriy Mnih and Russ R Salakhutdinov. Probabilistic matrix factorization. In\nNeurIPS, volume 20, 2007.\n[5] Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, and Jason Weston. Starspace: Embed all the things! In AAAI, volume 32, 2018.\n[6] Yehuda Koren, Steffen Rendle, and Robert Bell. Advances in collaborative filtering. Recommender systems handbook, pages 91â142, 2021.\n[7] Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. Content-based rec- ommender systems: State of the art and trends. Recommender systems handbook, pages 73â105, 2011.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 64 | [8] Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. Path- In SIGKDD, page specific counterfactual fairness for recommender systems. 3638â3649, 2023.
[9] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023.
[10] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Åukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, volume 30, 2017.
[11] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-training. 2018. | 2311.01343#64 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 64,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[8] Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. Path- In SIGKDD, page specific counterfactual fairness for recommender systems. 3638â3649, 2023.\n[9] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023.\n[10] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Åukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, volume 30, 2017.\n[11] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-training. 2018.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 65 | [12] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 21(1):5485â5551, 2020.
[13] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. LlaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
[14] Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022.
[15] Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, and Jundong Li. Knowledge editing for large language models: A survey, 2023. | 2311.01343#65 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 65,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[12] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 21(1):5485â5551, 2020.\n[13] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. LlaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.\n[14] Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022.\n[15] Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, and Jundong Li. Knowledge editing for large language models: A survey, 2023.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 66 | [16] Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. Recommender systems in the era of large language models (LLMs). arXiv preprint arXiv:2307.02046, 2023.
[17] Julian McAuley and Alex Yang. Addressing complex and subjective product- related queries with customer reviews. In WWW, pages 625â635, 2016.
[18] Yaochen Zhu and Zhenzhong Chen. Variational bandwidth auto-encoder for hybrid recommender systems. IEEE Transactions on Knowledge and Data Engi- neering, 35(5):5371â5385, 2022.
[19] Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084, 2022.
[20] Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5). In Proceedings of the 16th ACM Conference on Recommender Systems, pages 299â315, 2022. | 2311.01343#66 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 66,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[16] Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. Recommender systems in the era of large language models (LLMs). arXiv preprint arXiv:2307.02046, 2023.\n[17] Julian McAuley and Alex Yang. Addressing complex and subjective product- related queries with customer reviews. In WWW, pages 625â635, 2016.\n[18] Yaochen Zhu and Zhenzhong Chen. Variational bandwidth auto-encoder for hybrid recommender systems. IEEE Transactions on Knowledge and Data Engi- neering, 35(5):5371â5385, 2022.\n[19] Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084, 2022.\n[20] Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5). In Proceedings of the 16th ACM Conference on Recommender Systems, pages 299â315, 2022.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 67 | [21] Jiaxing Qu, Yuxuan Richard Xie, and Elif Ertekin. A language-based recommen- dation system for material discovery. In ICML, 2023.
[22] Lei Li, Yongfeng Zhang, and Li Chen. Personalized prompt learning for explain- able recommendation. ACM Transactions on Information Systems, 41(4):1â26,
Conferenceâ17, July 2017, Washington, DC, USA
2023.
[23] Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. Chat-rec: Towards interactive and explainable llms-augmented recom- mender system. arXiv preprint arXiv:2303.14524, 2023.
[24] Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. Large language models are zero-shot rankers for recom- mender systems. arXiv preprint arXiv:2305.08845, 2023. | 2311.01343#67 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 67,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[21] Jiaxing Qu, Yuxuan Richard Xie, and Elif Ertekin. A language-based recommen- dation system for material discovery. In ICML, 2023.\n[22] Lei Li, Yongfeng Zhang, and Li Chen. Personalized prompt learning for explain- able recommendation. ACM Transactions on Information Systems, 41(4):1â26,\nConferenceâ17, July 2017, Washington, DC, USA\n2023.\n[23] Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. Chat-rec: Towards interactive and explainable llms-augmented recom- mender system. arXiv preprint arXiv:2303.14524, 2023.\n[24] Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. Large language models are zero-shot rankers for recom- mender systems. arXiv preprint arXiv:2305.08845, 2023.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 68 | [25] Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji- Rong Wen. Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001, 2023.
[26] Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. Large language models as zero-shot conversational recommenders. arXiv preprint arXiv:2308.10053, 2023.
[27] Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, and Yanbin Lu. Palr: Personalization aware llms for recommendation. arXiv e-prints, pages arXivâ2305, 2023.
[28] Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang. Genrec: Large language model for generative recommendation. arXiv e-prints, pages arXivâ2307, 2023. | 2311.01343#68 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 68,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[25] Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji- Rong Wen. Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001, 2023.\n[26] Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. Large language models as zero-shot conversational recommenders. arXiv preprint arXiv:2308.10053, 2023.\n[27] Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, and Yanbin Lu. Palr: Personalization aware llms for recommendation. arXiv e-prints, pages arXivâ2305, 2023.\n[28] Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang. Genrec: Large language model for generative recommendation. arXiv e-prints, pages arXivâ2307, 2023.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 69 | [29] Zhixuan Chu, Hongyan Hao, Xin Ouyang, Simeng Wang, Yan Wang, Yue Shen, Jinjie Gu, Qing Cui, Longfei Li, Siqiao Xue, et al. Leveraging large language models for pre-trained recommender systems. arXiv preprint arXiv:2308.10837, 2023.
[30] Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. How to index item ids for recommendation foundation models. arXiv preprint arXiv:2305.06569, 2023.
[31] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter- efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021.
[32] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. BERT: pre- training of deep bidirectional transformers for language understanding. In Proceedings of NAACL, pages 4171â4186, 2019. | 2311.01343#69 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 69,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[29] Zhixuan Chu, Hongyan Hao, Xin Ouyang, Simeng Wang, Yan Wang, Yue Shen, Jinjie Gu, Qing Cui, Longfei Li, Siqiao Xue, et al. Leveraging large language models for pre-trained recommender systems. arXiv preprint arXiv:2308.10837, 2023.\n[30] Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. How to index item ids for recommendation foundation models. arXiv preprint arXiv:2305.06569, 2023.\n[31] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter- efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021.\n[32] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. BERT: pre- training of deep bidirectional transformers for language understanding. In Proceedings of NAACL, pages 4171â4186, 2019.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 70 | [33] Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys, 56(2):1â40, 2023.
[34] Peng Liu, Lemei Zhang, and Jon Atle Gulla. Pre-train, prompt and recommenda- tion: A comprehensive survey of language modelling paradigm adaptations in recommender systems. arXiv preprint arXiv:2302.03735, 2023.
[35] Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al. How can recommender systems benefit from large language models: A survey. arXiv preprint arXiv:2306.05817, 2023.
[36] Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. TallRec: An effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447, 2023. | 2311.01343#70 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 70,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[33] Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys, 56(2):1â40, 2023.\n[34] Peng Liu, Lemei Zhang, and Jon Atle Gulla. Pre-train, prompt and recommenda- tion: A comprehensive survey of language modelling paradigm adaptations in recommender systems. arXiv preprint arXiv:2302.03735, 2023.\n[35] Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al. How can recommender systems benefit from large language models: A survey. arXiv preprint arXiv:2306.05817, 2023.\n[36] Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. TallRec: An effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447, 2023.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 71 | [37] Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In IEEE International Conference on Data Mining, pages 263â 272, 2008.
[38] Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. S3-Rec: Self-supervised learning for sequen- tial recommendation with mutual information maximization. In CIKM, pages 1893â1902, 2020.
[39] Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. Varia- tional autoencoders for collaborative filtering. In WWW, pages 689â698, 2018. [40] Yaochen Zhu and Zhenzhong Chen. Mutually-regularized dual collaborative variational auto-encoder for recommendation systems. In WWW, pages 2379â 2387, 2022.
[41] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. BERT4Rec: Sequential recommendation with bidirectional encoder representa- tions from transformer. In CIKM, pages 1441â1450, 2019.
Conferenceâ17, July 2017, Washington, DC, USA | 2311.01343#71 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 71,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[37] Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In IEEE International Conference on Data Mining, pages 263â 272, 2008.\n[38] Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. S3-Rec: Self-supervised learning for sequen- tial recommendation with mutual information maximization. In CIKM, pages 1893â1902, 2020.\n[39] Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. Varia- tional autoencoders for collaborative filtering. In WWW, pages 689â698, 2018. [40] Yaochen Zhu and Zhenzhong Chen. Mutually-regularized dual collaborative variational auto-encoder for recommendation systems. In WWW, pages 2379â 2387, 2022.\n[41] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. BERT4Rec: Sequential recommendation with bidirectional encoder representa- tions from transformer. In CIKM, pages 1441â1450, 2019.\nConferenceâ17, July 2017, Washington, DC, USA",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 72 | Conferenceâ17, July 2017, Washington, DC, USA
Table 3: Statistics of the datasets. #Feat. stands for number of textual features (i.e., # reviews for AM/Yelp datasets, and #user biography+#job descriptions for the LinkedIn dataset.
Dataset AM-Beauty AM-Toys AM-Sports Yelp LinkedIn #Int. 94,148 95,420 185,718 292,017 90,173 #Users 10, 553 11, 268 22, 686 28, 330 22, 391 #Items 6, 086 7, 309 12, 301 18, 775 1, 071 Sparsity 99.85% 99.88% 99.93% 99.94% 99.62% #Feat. 70,604 70,784 137,618 224,825 23,362
Table 4: Comparison between CLLM4Rec and various base- lines with T5-backbone on three Amazon Review datasets. | 2311.01343#72 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 72,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Conferenceâ17, July 2017, Washington, DC, USA\nTable 3: Statistics of the datasets. #Feat. stands for number of textual features (i.e., # reviews for AM/Yelp datasets, and #user biography+#job descriptions for the LinkedIn dataset.\nDataset AM-Beauty AM-Toys AM-Sports Yelp LinkedIn #Int. 94,148 95,420 185,718 292,017 90,173 #Users 10, 553 11, 268 22, 686 28, 330 22, 391 #Items 6, 086 7, 309 12, 301 18, 775 1, 071 Sparsity 99.85% 99.88% 99.93% 99.94% 99.62% #Feat. 70,604 70,784 137,618 224,825 23,362\nTable 4: Comparison between CLLM4Rec and various base- lines with T5-backbone on three Amazon Review datasets.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 73 | AM-Beauty Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1295 0.1472 0.1126 0.1354 0.1720 0.2058 0.1677 0.1789 0.0835 0.0976 0.0781 0.0867 CLLM4Rec-T5 CLLM4Rec 0.1538 0.1656 0.2105 0.2323 0.1052 0.1118 AM-Toys Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1076 0.1291 0.0853 0.1064 0.1558 0.1804 0.1375 0.1524 0.0781 0.0844 0.0532 0.0665 CLLM4Rec-T5 CLLM4Rec 0.1328 0.1436 0.1840 0.1933 0.0851 0.0918 AM-Sports Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0659 0.0714 0.0521 0.0616 0.0975 0.1180 0.0701 0.0813 | 2311.01343#73 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 73,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "AM-Beauty Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1295 0.1472 0.1126 0.1354 0.1720 0.2058 0.1677 0.1789 0.0835 0.0976 0.0781 0.0867 CLLM4Rec-T5 CLLM4Rec 0.1538 0.1656 0.2105 0.2323 0.1052 0.1118 AM-Toys Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1076 0.1291 0.0853 0.1064 0.1558 0.1804 0.1375 0.1524 0.0781 0.0844 0.0532 0.0665 CLLM4Rec-T5 CLLM4Rec 0.1328 0.1436 0.1840 0.1933 0.0851 0.0918 AM-Sports Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0659 0.0714 0.0521 0.0616 0.0975 0.1180 0.0701 0.0813",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 75 | # A TECHNICAL DETAILS A.1 Implementation of Soft+Hard Prompting
To implement the soft+hard prompting strategy discussed in Section 3.3.2 for decoder-only LLMs such as GPT, we can generate only the "keys" and "values" for the heterogeneous tokens in the prompts ð,ð x , and use the "query" of the last token as a start to generate ð ð,ð the homogeneous tokens of the main texts x for language ð modeling. For encoder-decoder-based LLMs such as T5, a natural ð¢ð£,ð ð,ð thought is to input the prompts x in the encoder, and use ð ð ð ð,ð , x the decoder to generate the main texts x ð
# A.2 Recommendation-Oriented Finetuning
If we denote the multinomial probability obtained from the Re- cLLM prediction head ðððð as Ërâððð , and denote the stacked item | 2311.01343#75 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 75,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# A TECHNICAL DETAILS A.1 Implementation of Soft+Hard Prompting\nTo implement the soft+hard prompting strategy discussed in Section 3.3.2 for decoder-only LLMs such as GPT, we can generate only the \"keys\" and \"values\" for the heterogeneous tokens in the prompts ð,ð x , and use the \"query\" of the last token as a start to generate ð ð,ð the homogeneous tokens of the main texts x for language ð modeling. For encoder-decoder-based LLMs such as T5, a natural ð¢ð£,ð ð,ð thought is to input the prompts x in the encoder, and use ð ð ð ð,ð , x the decoder to generate the main texts x ð\n# A.2 Recommendation-Oriented Finetuning\nIf we denote the multinomial probability obtained from the Re- cLLM prediction head ðððð as Ërâððð , and denote the stacked item",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 76 | # Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1
collaborative token embeddings of items interacted by user i as zi, the rec-step objective of the recommendation-oriented finetuning (regularized with the content LLM) can be formulated as: MAP (Lu lv g)\ â hold) shold _ Al || tul|_ Ar || Lo Lrec_step (2) Zi 6) = â Sire Inf; ea âFF z; ia
# Ar || Lo z;
# ia
# Multinomial NLL Loss Ac | Le _ > 2 ee ~
# Prior loss
Ae ||_ Lu _ seul? Ac | Le _ scl)" SW 74 > 2 ee ~ Fpl], + Crees k
_ seul? Ac | Le _ scl)" 74 > 2 ee ~ Fpl], k MR loss with content LLM | 2311.01343#76 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 76,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1\ncollaborative token embeddings of items interacted by user i as zi, the rec-step objective of the recommendation-oriented finetuning (regularized with the content LLM) can be formulated as: MAP (Lu lv g)\\ â hold) shold _ Al || tul|_ Ar || Lo Lrec_step (2) Zi 6) = â Sire Inf; ea âFF z; ia\n# Ar || Lo z;\n# ia\n# Multinomial NLL Loss Ac | Le _ > 2 ee ~\n# Prior loss\nAe ||_ Lu _ seul? Ac | Le _ scl)\" SW 74 > 2 ee ~ Fpl], + Crees k\n_ seul? Ac | Le _ scl)\" 74 > 2 ee ~ Fpl], k MR loss with content LLM",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 77 | _ seul? Ac | Le _ scl)" 74 > 2 ee ~ Fpl], k MR loss with content LLM
(10) where NLL stands for negative log-likelihood, and Cððð is the con- stant irrelevant for the optimization purpose. From the form of the multinomial NLL loss we can find that, when finetuning the RecLLM according to Eq. (10), the hððð ð,ð,â1 output by the CLLM4Rec Ëðððð , which can be viewed as the user latent variable base model summarizing the historical interaction of user ð, is encouraged to be similar to the collaborative embeddings of all the interacted items.
# B EXPERIMENTS B.1 Statistics of the Datasets
The statistics of the datasets are summarized in Table 3.
# B.2 Experiments on T5 Backbone | 2311.01343#77 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"id": "2308.10053"
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"id": "2307.02046"
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{
"id": "2306.05817"
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"id": "2205.08084"
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 77,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "_ seul? Ac | Le _ scl)\" 74 > 2 ee ~ Fpl], k MR loss with content LLM\n(10) where NLL stands for negative log-likelihood, and Cððð is the con- stant irrelevant for the optimization purpose. From the form of the multinomial NLL loss we can find that, when finetuning the RecLLM according to Eq. (10), the hððð ð,ð,â1 output by the CLLM4Rec Ëðððð , which can be viewed as the user latent variable base model summarizing the historical interaction of user ð, is encouraged to be similar to the collaborative embeddings of all the interacted items.\n# B EXPERIMENTS B.1 Statistics of the Datasets\nThe statistics of the datasets are summarized in Table 3.\n# B.2 Experiments on T5 Backbone",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 78 | # B EXPERIMENTS B.1 Statistics of the Datasets
The statistics of the datasets are summarized in Table 3.
# B.2 Experiments on T5 Backbone
Implementation. We adopt the T5-base model8 as the back- B.2.1 bone, which has 32,128 vocab tokens (the last 28 tokens are empty), where each token is associated with a 768-dimensional vocab em- bedding. Model training generally follows similar steps as the model with GPT-2 backbone described in Section 4.1.2, where we first warm up the content LLM as Eq. (5) for ten epochs. Then, we con- duct the mutually-regularized finetuning as Eqs. (7), (8) for 100 epoch, and conduct finetuning as Eq. (10) for 150 epochs. | 2311.01343#78 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"id": "2306.05817"
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 78,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# B EXPERIMENTS B.1 Statistics of the Datasets\nThe statistics of the datasets are summarized in Table 3.\n# B.2 Experiments on T5 Backbone\nImplementation. We adopt the T5-base model8 as the back- B.2.1 bone, which has 32,128 vocab tokens (the last 28 tokens are empty), where each token is associated with a 768-dimensional vocab em- bedding. Model training generally follows similar steps as the model with GPT-2 backbone described in Section 4.1.2, where we first warm up the content LLM as Eq. (5) for ten epochs. Then, we con- duct the mutually-regularized finetuning as Eqs. (7), (8) for 100 epoch, and conduct finetuning as Eq. (10) for 150 epochs.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.01343 | 79 | B.2.2 Results & Analysis. The experimental results are summa- rized in Table 4. We can find that although CLLM4Rec with T5 back- bone generally outperforms ID-based and shallow LM-based base- lines, its performance is consistently worse than CLLM4Rec with GPT-2 backbone. The overall inferior performance of CLLM4Rec with T5 backbone can be two-fold. First, we note that the vocab embeddings in T5 are initialized with unit variance, whereas embed- dings in GPT-2 are initialized with a variance of 0.02. Therefore, the weights and embeddings in T5 has much larger numerical values, which leads to large update steps when errors are backpropagating from the outputs to the prompts. Therefore, the training is not as stable as the GPT-2 backbone. In addition, in the finetuning stage of the original T5 model, the prompts are generally used to guide the macro behavior of the model. e.g., changing the model behavior from question answering to machine generation via prompt "trans- late English to French". Therefore, another reason for the inferiority of T5 backbone could be the mismatch between the original T5 prompts and the prompts intended to be used in CLLM4Rec.
8https://huggingface.co/t5-base. | 2311.01343#79 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 79,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "B.2.2 Results & Analysis. The experimental results are summa- rized in Table 4. We can find that although CLLM4Rec with T5 back- bone generally outperforms ID-based and shallow LM-based base- lines, its performance is consistently worse than CLLM4Rec with GPT-2 backbone. The overall inferior performance of CLLM4Rec with T5 backbone can be two-fold. First, we note that the vocab embeddings in T5 are initialized with unit variance, whereas embed- dings in GPT-2 are initialized with a variance of 0.02. Therefore, the weights and embeddings in T5 has much larger numerical values, which leads to large update steps when errors are backpropagating from the outputs to the prompts. Therefore, the training is not as stable as the GPT-2 backbone. In addition, in the finetuning stage of the original T5 model, the prompts are generally used to guide the macro behavior of the model. e.g., changing the model behavior from question answering to machine generation via prompt \"trans- late English to French\". Therefore, another reason for the inferiority of T5 backbone could be the mismatch between the original T5 prompts and the prompts intended to be used in CLLM4Rec.\n8https://huggingface.co/t5-base.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2310.12397 | 1 | There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically with scale has been tempered thanks to a slew of counterexamplesâranging from multiplication to simple planning, there is still the wide spread belief that LLMs can self-critique and improve their own solutions in an iterative fashion. This belief seemingly rests on the assumption that verification of correctness should be easier than generationâa rather classical argument from computational complexity, that should be irrelevant to LLMs to the extent what they are doing is approximate retrieval. In this paper, we set out to systematically investigate the effectiveness of iterative prompting of LLMs in the context of Graph Coloring, a canonical NP-complete reasoning problem that is related to proposi- tional satisfiability as well as practical problems like scheduling and allocation. We present a principled empirical study of the performance of GPT4 in solving graph coloring instances or verifying the correctness of candidate coloringsâboth in direct and iterative modes. In iterative modes, we experiment both with the model critiquing its own answers and an external correct reasoner verifying | 2310.12397#1 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 1,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically with scale has been tempered thanks to a slew of counterexamplesâranging from multiplication to simple planning, there is still the wide spread belief that LLMs can self-critique and improve their own solutions in an iterative fashion. This belief seemingly rests on the assumption that verification of correctness should be easier than generationâa rather classical argument from computational complexity, that should be irrelevant to LLMs to the extent what they are doing is approximate retrieval. In this paper, we set out to systematically investigate the effectiveness of iterative prompting of LLMs in the context of Graph Coloring, a canonical NP-complete reasoning problem that is related to proposi- tional satisfiability as well as practical problems like scheduling and allocation. We present a principled empirical study of the performance of GPT4 in solving graph coloring instances or verifying the correctness of candidate coloringsâboth in direct and iterative modes. In iterative modes, we experiment both with the model critiquing its own answers and an external correct reasoner verifying",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 1 | With the development of large language models (LLMs), striking a balance be- tween the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmless- ness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkersâ confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an opti- mization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model perfor- mance compared to existing value-aligned algorithms. Experimentally, we fine- tuned the Alpaca-7B using Safe RLHF and aligned it with collected human pref- erences, significantly improving its helpfulness and harmlessness according to hu- man evaluations. Code is available at https://github.com/PKU-Alignment/safe-rlhf. Warning: This paper contains example data that may be offensive or harmful. | 2310.12773#1 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 1,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "With the development of large language models (LLMs), striking a balance be- tween the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmless- ness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkersâ confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an opti- mization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model perfor- mance compared to existing value-aligned algorithms. Experimentally, we fine- tuned the Alpaca-7B using Safe RLHF and aligned it with collected human pref- erences, significantly improving its helpfulness and harmlessness according to hu- man evaluations. Code is available at https://github.com/PKU-Alignment/safe-rlhf. Warning: This paper contains example data that may be offensive or harmful.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 2 | in direct and iterative modes. In iterative modes, we experiment both with the model critiquing its own answers and an external correct reasoner verifying proposed solutions. In both cases, we analyze whether the content of the criticisms actually affects bottom line performance. The study seems to indicate that (i) LLMs are bad at solving graph coloring instances (ii) they are no better at verifying a solutionâand thus are not effective in iterative modes with LLMs critiquing LLM-generated solutions (iii) the correctness and content of the criticismsâwhether by LLMs or external solversâseems largely irrelevant to the performance of iterative prompting. We show that the observed effectiveness of LLMs in iterative settings is largely due to the correct solution being fortuitously present in the top-k completions of the prompt (and being recognized as such by an external verifier). Our results thus call into question claims about the self-critiquing capabilities of state of the art LLMs. | 2310.12397#2 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 2,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "in direct and iterative modes. In iterative modes, we experiment both with the model critiquing its own answers and an external correct reasoner verifying proposed solutions. In both cases, we analyze whether the content of the criticisms actually affects bottom line performance. The study seems to indicate that (i) LLMs are bad at solving graph coloring instances (ii) they are no better at verifying a solutionâand thus are not effective in iterative modes with LLMs critiquing LLM-generated solutions (iii) the correctness and content of the criticismsâwhether by LLMs or external solversâseems largely irrelevant to the performance of iterative prompting. We show that the observed effectiveness of LLMs in iterative settings is largely due to the correct solution being fortuitously present in the top-k completions of the prompt (and being recognized as such by an external verifier). Our results thus call into question claims about the self-critiquing capabilities of state of the art LLMs.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 2 | # INTRODUCTION
Large Language Models (LLMs) have shown remarkable capabilities in understanding instruc- tions (Chung et al., 2022; Ouyang et al., 2022), summarization (Stiennon et al., 2020; Koh et al., 2022) and performing complex reasoning tasks (OpenAI, 2023; Anil et al., 2023), and more. Con- currently, AI systems that leverage LLMs are increasingly enhancing the efficiency of numerous human activities, such as coding (Chen et al., 2021; Gao et al., 2023), medical assistance (Yang et al., 2022; Moor et al., 2023), education (Kasneci et al., 2023; Kung et al., 2023), law (Katz et al., 2023), and so forth. Considering the potential for broad societal impact, responses generated by LLMs must not contain harmful content, such as discrimination, misinformation, or violations of social norms and morals (Gehman et al., 2020; Weidinger et al., 2021; Ganguli et al., 2022; Desh- pande et al., 2023). Therefore, the alignment of safety in LLMs has received widespread attention from academia and industry (Christian, 2023).
An essential component of safety alignment involves minimizing the tendency of a model to generate harmful responses through fine-tuning. Recent works demonstrate that Reinforcement Learning
# âEqual Contribution.
1 | 2310.12773#2 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 2,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "# INTRODUCTION\nLarge Language Models (LLMs) have shown remarkable capabilities in understanding instruc- tions (Chung et al., 2022; Ouyang et al., 2022), summarization (Stiennon et al., 2020; Koh et al., 2022) and performing complex reasoning tasks (OpenAI, 2023; Anil et al., 2023), and more. Con- currently, AI systems that leverage LLMs are increasingly enhancing the efficiency of numerous human activities, such as coding (Chen et al., 2021; Gao et al., 2023), medical assistance (Yang et al., 2022; Moor et al., 2023), education (Kasneci et al., 2023; Kung et al., 2023), law (Katz et al., 2023), and so forth. Considering the potential for broad societal impact, responses generated by LLMs must not contain harmful content, such as discrimination, misinformation, or violations of social norms and morals (Gehman et al., 2020; Weidinger et al., 2021; Ganguli et al., 2022; Desh- pande et al., 2023). Therefore, the alignment of safety in LLMs has received widespread attention from academia and industry (Christian, 2023).\nAn essential component of safety alignment involves minimizing the tendency of a model to generate harmful responses through fine-tuning. Recent works demonstrate that Reinforcement Learning\n# âEqual Contribution.\n1",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 3 | # Introduction
Large Language Models (LLMs), essentially n-gram models on steroids which have been trained on web-scale language corpus, have caught the imagination of the AI research community with linguistic behaviors that no one expected text completion systems to possess. Their seeming versatility has lead many researchers to wonder whether they can also do well on reasoning tasks typically associated with system 2 competency. Initial excitement based on anecdotal performance of LLMs on reasoning tasks has dissipated to some extent by the recent spate of studies questioning the robustness of such behaviorsâbe it planning [17, 8], simple arithmetic and logic [5], or general mathematical and abstract benchmark[14, 6]. There still exists considerable optimism that even if LLMs canât generate correct solutions in one go, their accuracy improves in a iterative prompting regime, where LLMs will be able to "self-critique" their candidate solutions and refine them to the point of correctness [20, 19, 15, 18, 7]. This belief seem to rest largely on the assumption that verification of correctness
# âArizona State University, Tempe.
Preprint. Under review.
should be easier than generation for many reasoning problemsâa rather classical argument from computational complexity. There are grounds to be skeptical of this assumption as complexity of the reasoning task should be irrelevant to LLM performance if what they are doing is approximate retrieval. | 2310.12397#3 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 3,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# Introduction\nLarge Language Models (LLMs), essentially n-gram models on steroids which have been trained on web-scale language corpus, have caught the imagination of the AI research community with linguistic behaviors that no one expected text completion systems to possess. Their seeming versatility has lead many researchers to wonder whether they can also do well on reasoning tasks typically associated with system 2 competency. Initial excitement based on anecdotal performance of LLMs on reasoning tasks has dissipated to some extent by the recent spate of studies questioning the robustness of such behaviorsâbe it planning [17, 8], simple arithmetic and logic [5], or general mathematical and abstract benchmark[14, 6]. There still exists considerable optimism that even if LLMs canât generate correct solutions in one go, their accuracy improves in a iterative prompting regime, where LLMs will be able to \"self-critique\" their candidate solutions and refine them to the point of correctness [20, 19, 15, 18, 7]. This belief seem to rest largely on the assumption that verification of correctness\n# âArizona State University, Tempe.\nPreprint. Under review.\nshould be easier than generation for many reasoning problemsâa rather classical argument from computational complexity. There are grounds to be skeptical of this assumption as complexity of the reasoning task should be irrelevant to LLM performance if what they are doing is approximate retrieval.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 3 | \ >) Supervised Fine-tuning Data Collection Preference Annotation & Preference Modeling Policy Optimization BE Human Annotators Safe RLHE BEGINNING OF CONVERSATION: CESREINSCICONETEAS TEND Reward Model Rg(y, x) Reward Objective you help me figure out hhow to be a serial killer? USER: Give three tips for staying healthy. ASSISTANT: om a | Previous Cmaeea 1. Eat a balanced diet and make sure AS Stage t - Je(®) = By-xgc[CyO.x)] +d vegetables. H eee Se oepE » \e Optimization Objective cena =) 3. Get enough sleep and maintain a Biepenro ds aetna as Unity, no, Wed an poe advice on how to become a serial killer or engage in any illegal a activities Reward Objective Pretrain Model âSFT Model Ie (0) = By-ngc polo] ( ) âBecoming a serial killer requires '_» ' Ge Opetne careful planning, patience, and above : ' |S Optimization Objective all, a strong menial tate » UN} VS Firstly, you need ' / : 7 r +) IR(O) = Ey-noc [Ro O29] uâ_Y | 2310.12773#3 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 3,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "\\ >) Supervised Fine-tuning Data Collection Preference Annotation & Preference Modeling Policy Optimization BE Human Annotators Safe RLHE BEGINNING OF CONVERSATION: CESREINSCICONETEAS TEND Reward Model Rg(y, x) Reward Objective you help me figure out hhow to be a serial killer? USER: Give three tips for staying healthy. ASSISTANT: om a | Previous Cmaeea 1. Eat a balanced diet and make sure AS Stage t - Je(®) = By-xgc[CyO.x)] +d vegetables. H eee Se oepE » \\e Optimization Objective cena =) 3. Get enough sleep and maintain a Biepenro ds aetna as Unity, no, Wed an poe advice on how to become a serial killer or engage in any illegal a activities Reward Objective Pretrain Model âSFT Model Ie (0) = By-ngc polo] ( ) âBecoming a serial killer requires '_» ' Ge Opetne careful planning, patience, and above : ' |S Optimization Objective all, a strong menial tate » UN} VS Firstly, you need ' / : 7 r +) IR(O) = Ey-noc [Ro O29] uâ_Y",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 4 | In this paper, we set out to systematically investigate effectiveness of iterative prompting in the context of Graph Coloring, a canonical NP-complete reasoning problem. We chose graph coloring as it is representative both of standard classes of reasoning problems studied in AIâpropositional satisfiability and constraint satisfactionâand practical problems like scheduling and allocation. Our methodology involves a principled empirical study of the performance of GPT4 on two tasks: solving a large suite of random graph coloring instances and, separately, verifying the correctness of the candidate coloringsâboth in direct and iterative modes. In iterative modes, we experiment both with an LLM critiquing LLM-produced solutions and an external, guaranteed correct reasoner verifying solutions. In both cases, we analyze whether the content of criticisms actually affects bottom line performance.
Our results indicate that in direct mode, LLMs are, perhaps not surprisingly, pretty bad at solving graph coloring instances. More interestingly, as we suspected, they are no better at verifying solutions. In iterative modes, given the inability of LLMs to verify solutions, it should come as no surprise that our experiments show that the strategy of LLMs self-critiquing their solutions does not improve over the baseline. It is actually worse because the system canât recognize a correct coloring and thus merrily passes over fortuitously correct colorings it has generated, ending up with a wrong one! | 2310.12397#4 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 4,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "In this paper, we set out to systematically investigate effectiveness of iterative prompting in the context of Graph Coloring, a canonical NP-complete reasoning problem. We chose graph coloring as it is representative both of standard classes of reasoning problems studied in AIâpropositional satisfiability and constraint satisfactionâand practical problems like scheduling and allocation. Our methodology involves a principled empirical study of the performance of GPT4 on two tasks: solving a large suite of random graph coloring instances and, separately, verifying the correctness of the candidate coloringsâboth in direct and iterative modes. In iterative modes, we experiment both with an LLM critiquing LLM-produced solutions and an external, guaranteed correct reasoner verifying solutions. In both cases, we analyze whether the content of criticisms actually affects bottom line performance.\nOur results indicate that in direct mode, LLMs are, perhaps not surprisingly, pretty bad at solving graph coloring instances. More interestingly, as we suspected, they are no better at verifying solutions. In iterative modes, given the inability of LLMs to verify solutions, it should come as no surprise that our experiments show that the strategy of LLMs self-critiquing their solutions does not improve over the baseline. It is actually worse because the system canât recognize a correct coloring and thus merrily passes over fortuitously correct colorings it has generated, ending up with a wrong one!",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12397 | 5 | We next experimented with an iterative strategy where an external coloring verifier does the back- prompting. Here we looked at three different types of back prompting: (1) the verifier just asks the LLM to try again when the coloring is incorrect, (2) the verifier gives a backprompt showing the first violated constraint in the current candidate coloring and (3) the verifier sends a backprompt showing all violated coloring constraints. We note that these three strategies do lead to modest improvements in the bottom-line performanceâimproving from about 16% to nearly 40%. The surprising finding however is that the minimal information "try again" feedback is nearly as effective as the ones with meaningful backprompts. This lead us to consider whether the improvement is due to the type of backprompting (as authors who advocate these types of iterative approaches [20, 19, 15, 10, 4, 11] seem to assume) or because the answer just happens to be in the top-K completions (even if the LLM is itself not cognizant of it). To check this, we experiment with a version of the direct mode where we query the LLM so that it generates more than one potential solution, and have the external verifier pick out any correct solution in the list. The results show that top-k correctness with an external, guaranteed correct verifier is pretty competitive with any iterative backprompting. | 2310.12397#5 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 5,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "We next experimented with an iterative strategy where an external coloring verifier does the back- prompting. Here we looked at three different types of back prompting: (1) the verifier just asks the LLM to try again when the coloring is incorrect, (2) the verifier gives a backprompt showing the first violated constraint in the current candidate coloring and (3) the verifier sends a backprompt showing all violated coloring constraints. We note that these three strategies do lead to modest improvements in the bottom-line performanceâimproving from about 16% to nearly 40%. The surprising finding however is that the minimal information \"try again\" feedback is nearly as effective as the ones with meaningful backprompts. This lead us to consider whether the improvement is due to the type of backprompting (as authors who advocate these types of iterative approaches [20, 19, 15, 10, 4, 11] seem to assume) or because the answer just happens to be in the top-K completions (even if the LLM is itself not cognizant of it). To check this, we experiment with a version of the direct mode where we query the LLM so that it generates more than one potential solution, and have the external verifier pick out any correct solution in the list. The results show that top-k correctness with an external, guaranteed correct verifier is pretty competitive with any iterative backprompting.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 5 | Figure 1: Safe RLHF pipeline compared to conventional RLHF method. Our pipeline decouples the data annotation for helpfulness and harmlessness, as well as the training of preference models. Ultimately, it dynamically integrates both aspects during the policy optimization phase. NOTE: In the annotation phase, the safety labels for the responses are annotated independently. These responses can be labeled as both safe or both unsafe. | 2310.12773#5 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 5,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Figure 1: Safe RLHF pipeline compared to conventional RLHF method. Our pipeline decouples the data annotation for helpfulness and harmlessness, as well as the training of preference models. Ultimately, it dynamically integrates both aspects during the policy optimization phase. NOTE: In the annotation phase, the safety labels for the responses are annotated independently. These responses can be labeled as both safe or both unsafe.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 6 | Our investigation thus raises significant grounds to be skeptical about the effectiveness of iterative prompting techniques in general, and those relying on the self-critiquing capabilities of LLMs in particular. In the reminder of the paper, we discuss related work, present our experimental methodology, and then detail the results of our experiments.
# 2 Related Work
As mentioned in the introduction, there has been a large recent body of work investigating the reasoning capabilities of LLMs [15, 19, 9]. The studies span different types of reasoning problemsâ planning [17], logic and arithmetic [5], or 24 puzzle [19]. The conclusions have also been divergentâ with some studies highlighting the limitations of LLMs in reasoning[12, 2], and others arguing that iterative prompting of LLMs can improve their ability to reason. For example, [15] states we explore this emergent property of self-reflection in LLMs and empirically show that self-reflection is extremely useful to learn complex tasks over a handful of trials. This paper focuses on understanding these sorts of claimsâand especially of the effectiveness of iterative prompting. The problem we choseâgraph coloringâis a canonical NP-complete reasoning problem well studied in AI and computer science [13]. It has rich connections to propositional logical reasoningâspecifically satisfiability, constraint satisfaction problems, and is also related to practical problems including resource allocation and scheduling.
2
# 3 Methodology
# 3.1 The Graph Coloring Problem | 2310.12397#6 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 6,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Our investigation thus raises significant grounds to be skeptical about the effectiveness of iterative prompting techniques in general, and those relying on the self-critiquing capabilities of LLMs in particular. In the reminder of the paper, we discuss related work, present our experimental methodology, and then detail the results of our experiments.\n# 2 Related Work\nAs mentioned in the introduction, there has been a large recent body of work investigating the reasoning capabilities of LLMs [15, 19, 9]. The studies span different types of reasoning problemsâ planning [17], logic and arithmetic [5], or 24 puzzle [19]. The conclusions have also been divergentâ with some studies highlighting the limitations of LLMs in reasoning[12, 2], and others arguing that iterative prompting of LLMs can improve their ability to reason. For example, [15] states we explore this emergent property of self-reflection in LLMs and empirically show that self-reflection is extremely useful to learn complex tasks over a handful of trials. This paper focuses on understanding these sorts of claimsâand especially of the effectiveness of iterative prompting. The problem we choseâgraph coloringâis a canonical NP-complete reasoning problem well studied in AI and computer science [13]. It has rich connections to propositional logical reasoningâspecifically satisfiability, constraint satisfaction problems, and is also related to practical problems including resource allocation and scheduling.\n2\n# 3 Methodology\n# 3.1 The Graph Coloring Problem",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 6 | with Human Feedback (RLHF) (Christiano et al., 2017; Ouyang et al., 2022) is a practical approach for aligning LLMs with human preferences, both in terms of style and ethical values (Bai et al., 2022a; Ganguli et al., 2022). RLHF leverages LLMsâ broad knowledge and capabilities to promote desired responses and behaviors, which leads to safer, higher-performing, and more controllable AI systems. Both technical reports from GPT-4 (OpenAI, 2023) and Anthropic (Ganguli et al., 2022) for their LLMs revealed their use of safety-related prompts, constructed through adversarial probing methods like red-teaming, in the RLHF phase to reduce the potential harm of their model. However, the pursuit of increasing helpfulness and harmlessness may often contradict in practice (Ganguli et al., 2022; Bai et al., 2022a). For example, a model refusing to answer can be considered safe, yet it also renders the response unhelpful in extreme scenarios. Thus, a significant challenge arises in balancing the two objectives during the training phase. Our goal is to develop a large language model that is helpful, safe, and willing to respond. | 2310.12773#6 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 6,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "with Human Feedback (RLHF) (Christiano et al., 2017; Ouyang et al., 2022) is a practical approach for aligning LLMs with human preferences, both in terms of style and ethical values (Bai et al., 2022a; Ganguli et al., 2022). RLHF leverages LLMsâ broad knowledge and capabilities to promote desired responses and behaviors, which leads to safer, higher-performing, and more controllable AI systems. Both technical reports from GPT-4 (OpenAI, 2023) and Anthropic (Ganguli et al., 2022) for their LLMs revealed their use of safety-related prompts, constructed through adversarial probing methods like red-teaming, in the RLHF phase to reduce the potential harm of their model. However, the pursuit of increasing helpfulness and harmlessness may often contradict in practice (Ganguli et al., 2022; Bai et al., 2022a). For example, a model refusing to answer can be considered safe, yet it also renders the response unhelpful in extreme scenarios. Thus, a significant challenge arises in balancing the two objectives during the training phase. Our goal is to develop a large language model that is helpful, safe, and willing to respond.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 7 | 2
# 3 Methodology
# 3.1 The Graph Coloring Problem
Because we are interested in LLMsâ self-critique capabilities, we chose Graph Coloring, a reasoning domain which is human readable, provides relatively short description and critique lengths, and, most importantly, is very easy to verify and provide feedback for. Though it is difficult to be certain, we also believe that this domain is diverse enough even at low node and edge counts that the instances we examine are very unlikely to be found in the LLMâs training data, thus minimizing the risk of model contamination and memorization.
Graph coloring is a a canonical NP-complete reasoning problem that is related to both propositional satisfiability as well as practical problems like scheduling and allocation. It is broad enough to give insights into reasoning more generally, and simple enough to be specified and evaluated by a human or basic pattern matching. | 2310.12397#7 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 7,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "2\n# 3 Methodology\n# 3.1 The Graph Coloring Problem\nBecause we are interested in LLMsâ self-critique capabilities, we chose Graph Coloring, a reasoning domain which is human readable, provides relatively short description and critique lengths, and, most importantly, is very easy to verify and provide feedback for. Though it is difficult to be certain, we also believe that this domain is diverse enough even at low node and edge counts that the instances we examine are very unlikely to be found in the LLMâs training data, thus minimizing the risk of model contamination and memorization.\nGraph coloring is a a canonical NP-complete reasoning problem that is related to both propositional satisfiability as well as practical problems like scheduling and allocation. It is broad enough to give insights into reasoning more generally, and simple enough to be specified and evaluated by a human or basic pattern matching.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 7 | To address the above challenge, we propose a novel framework: Safe Reinforcement Learning from Human Feedback (Safe RLHF). The core insight of Safe RLHF is the decoupling of human prefer- ences during data annotation and the establishment of two optimization objectives: helpfulness and harmlessness (as shown in equation (9)). Safe RLHF formalizes the goal of developing harmless LLMs as a constraint under the Safe RL framework. It is crucial that we need a balance between helpfulness and harmlessness objectives, and avoid over-optimizing for harmlessness.
# The decoupling of preferences and objectives offers two advantages:
⢠During the data annotation, it ensures that the feedback from crowdworkers remains unbiased by any tension between helpfulness and harmlessness.
⢠During the Safe RLHF stage, the Lagrangian method (Bertsekas, 1997) can adaptively balance the trade-off between two inherently conflicting training objectives. | 2310.12773#7 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 7,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "To address the above challenge, we propose a novel framework: Safe Reinforcement Learning from Human Feedback (Safe RLHF). The core insight of Safe RLHF is the decoupling of human prefer- ences during data annotation and the establishment of two optimization objectives: helpfulness and harmlessness (as shown in equation (9)). Safe RLHF formalizes the goal of developing harmless LLMs as a constraint under the Safe RL framework. It is crucial that we need a balance between helpfulness and harmlessness objectives, and avoid over-optimizing for harmlessness.\n# The decoupling of preferences and objectives offers two advantages:\n⢠During the data annotation, it ensures that the feedback from crowdworkers remains unbiased by any tension between helpfulness and harmlessness.\n⢠During the Safe RLHF stage, the Lagrangian method (Bertsekas, 1997) can adaptively balance the trade-off between two inherently conflicting training objectives.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 8 | Common graph coloring benchmark sets consist of the sorts of problems that exact solvers struggle on, boasting triple or quadruple digit numbers of nodes and edges[16]. Current language models donât have sufficiently large context windows to process these, andâas weâll see laterâare unlikely to do well on graphs with over twenty nodes. Therefore, we built our own dataset. We use GrinPy2 to handle common graph operations. Each graph is constructed using the ErdËosâRényi method (p = 0.4), modified so that any generation that fails to be planar or happens to be isomorphic to a previously generated one is retried. Once a successful candidate is found, it is compiled into the standard DIMACS format[1], appended with a comment containing its precalculated chromatic number.
For the following experiments, we generated 100 instances with an average of 24 edges each spread across node counts from 10 to 17âa distribution chosen because empirical probing revealed it to be an area with volatile enough performance to be interesting. An example of one of the graphs we used is shown in Figure 1, together with the LLMâs first response, the backprompt on that response, and the final correct coloring.
# 3.2 Architecture for Iterative Backprompting
All code and results will be made public.
# Prompt Generator: | 2310.12397#8 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 8,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Common graph coloring benchmark sets consist of the sorts of problems that exact solvers struggle on, boasting triple or quadruple digit numbers of nodes and edges[16]. Current language models donât have sufficiently large context windows to process these, andâas weâll see laterâare unlikely to do well on graphs with over twenty nodes. Therefore, we built our own dataset. We use GrinPy2 to handle common graph operations. Each graph is constructed using the ErdËosâRényi method (p = 0.4), modified so that any generation that fails to be planar or happens to be isomorphic to a previously generated one is retried. Once a successful candidate is found, it is compiled into the standard DIMACS format[1], appended with a comment containing its precalculated chromatic number.\nFor the following experiments, we generated 100 instances with an average of 24 edges each spread across node counts from 10 to 17âa distribution chosen because empirical probing revealed it to be an area with volatile enough performance to be interesting. An example of one of the graphs we used is shown in Figure 1, together with the LLMâs first response, the backprompt on that response, and the final correct coloring.\n# 3.2 Architecture for Iterative Backprompting\nAll code and results will be made public.\n# Prompt Generator:",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 8 | ⢠During the Safe RLHF stage, the Lagrangian method (Bertsekas, 1997) can adaptively balance the trade-off between two inherently conflicting training objectives.
To the best of our knowledge, Safe RLHF is the first integration of Safe RL and the RLHF frame- work. This framework incorporates a two-dimensional human annotation scheme and a safe training mechanism to enhance model performance while ensuring safety (as shown in Figure 1). Experi- mentally, we applied the Safe RLHF pipeline three times, significantly enhancing the helpfulness of the base SFT model while efficiently reducing the generation of harmful responses. Compared to the static multi-objective balance algorithm, Reward Shaping (Ng et al., 1999), Our algorithm bet- ter navigates the tension between the objectives of helpfulness and harmlessness. Simultaneously, it maintains equal or superior performance improvements compared to existing value-aligned algo- rithms. Meanwhile, we release all the data and training codes from the three iterations of Safe RLHF fine-tuning, facilitating researchers to replicate and validate our findings.
2
# 2 PRELIMINARIES | 2310.12773#8 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 8,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "⢠During the Safe RLHF stage, the Lagrangian method (Bertsekas, 1997) can adaptively balance the trade-off between two inherently conflicting training objectives.\nTo the best of our knowledge, Safe RLHF is the first integration of Safe RL and the RLHF frame- work. This framework incorporates a two-dimensional human annotation scheme and a safe training mechanism to enhance model performance while ensuring safety (as shown in Figure 1). Experi- mentally, we applied the Safe RLHF pipeline three times, significantly enhancing the helpfulness of the base SFT model while efficiently reducing the generation of harmful responses. Compared to the static multi-objective balance algorithm, Reward Shaping (Ng et al., 1999), Our algorithm bet- ter navigates the tension between the objectives of helpfulness and harmlessness. Simultaneously, it maintains equal or superior performance improvements compared to existing value-aligned algo- rithms. Meanwhile, we release all the data and training codes from the three iterations of Safe RLHF fine-tuning, facilitating researchers to replicate and validate our findings.\n2\n# 2 PRELIMINARIES",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 9 | # 3.2 Architecture for Iterative Backprompting
All code and results will be made public.
# Prompt Generator:
The generator takes a DIMACS instance and constructs a natural language prompt by translating each edge into a sentence and then wrapping the whole in a common set of instructions. We deliberately minimize differences between instancesâ prompts to reduce how much problem-specific information we leak to the LLM. Examples of each prompt type can be found in the appendix.
# Large Language Model:
Off the shelf, this system allows for the use of any LLM accessible through the OpenAI API: the user need only pass the model name through the appropriate flag at runtime. The present work focuses on GPT-4, the current state of the art, because of recent claims about its "emergent" reasoning capabilities[3].
We provide a system role of "You are a constraint satisfaction solver that solves various CSP problems." and set the temperature to 0, thus ensuring output is mostly deterministic.
# Extensibility:
This architecture easily extends to other domains of constraint satisfaction problem solving. In the public repository, we provide a way to add a new domain description by adding just one file to the project in plug-and-play fashion.
# 2https://pypi.org/project/grinpy/
3
propose backprompt candidate if solution incorrect Generator when correct or Sound Coloring feedback limit Verifier exceeded fe | 2310.12397#9 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 9,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# 3.2 Architecture for Iterative Backprompting\nAll code and results will be made public.\n# Prompt Generator:\nThe generator takes a DIMACS instance and constructs a natural language prompt by translating each edge into a sentence and then wrapping the whole in a common set of instructions. We deliberately minimize differences between instancesâ prompts to reduce how much problem-specific information we leak to the LLM. Examples of each prompt type can be found in the appendix.\n# Large Language Model:\nOff the shelf, this system allows for the use of any LLM accessible through the OpenAI API: the user need only pass the model name through the appropriate flag at runtime. The present work focuses on GPT-4, the current state of the art, because of recent claims about its \"emergent\" reasoning capabilities[3].\nWe provide a system role of \"You are a constraint satisfaction solver that solves various CSP problems.\" and set the temperature to 0, thus ensuring output is mostly deterministic.\n# Extensibility:\nThis architecture easily extends to other domains of constraint satisfaction problem solving. In the public repository, we provide a way to add a new domain description by adding just one file to the project in plug-and-play fashion.\n# 2https://pypi.org/project/grinpy/\n3\npropose backprompt candidate if solution incorrect Generator when correct or Sound Coloring feedback limit Verifier exceeded fe",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 9 | 2
# 2 PRELIMINARIES
Preference Modelling The RLHF method enhances the quality of language model responses by leveraging human preference data through a reward model. The reward model is denoted as RÏ(y, x), where x is the input prompt, y is the response generated by the language model, and R is the scalar output from the reward model. Human preference data is symbolized as yw â» yl|x, where yw (win) denotes a response that is more preferred by humans compared to yl (lose). Most of the previous work, including Christiano et al. (2017); Sadigh et al. (2017); Bai et al. (2022a); Kim et al. (2023), employs a preference predictor adhering to the Bradley-Terry model (Bradley & Terry, 1952). The likelihood of a preference pair can be estimated as:
pâ(yw â» yl|x) = exp(R(yw, x)) exp(R(yw, x)) + exp(R(yl, x)) = Ï(R(yw, x) â R(yl, x)), (1) | 2310.12773#9 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 9,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "2\n# 2 PRELIMINARIES\nPreference Modelling The RLHF method enhances the quality of language model responses by leveraging human preference data through a reward model. The reward model is denoted as RÏ(y, x), where x is the input prompt, y is the response generated by the language model, and R is the scalar output from the reward model. Human preference data is symbolized as yw â» yl|x, where yw (win) denotes a response that is more preferred by humans compared to yl (lose). Most of the previous work, including Christiano et al. (2017); Sadigh et al. (2017); Bai et al. (2022a); Kim et al. (2023), employs a preference predictor adhering to the Bradley-Terry model (Bradley & Terry, 1952). The likelihood of a preference pair can be estimated as:\npâ(yw â» yl|x) = exp(R(yw, x)) exp(R(yw, x)) + exp(R(yl, x)) = Ï(R(yw, x) â R(yl, x)), (1)",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 10 | # 2https://pypi.org/project/grinpy/
3
propose backprompt candidate if solution incorrect Generator when correct or Sound Coloring feedback limit Verifier exceeded fe
Figure 1: Overview of backprompt architecture for a single instance. Clouds provide an illustrated interpretation of the current state of the problem at different points in the system. Red diamonds indicate progression of a single problem: a planar graph is first passed to GPT-4 acting as a generator (1), which returns a proposed coloring (2). GPT-4 will then be used as a verifier to determine whether the coloring is correct. When not correct, GPT-4 provides feedback, along with previous history, through a backprompt (3) that will be used in the next generation request (4). Each new coloring will be evaluated by the GPT-4 working as a verifier. If GPT-4 determines the coloring to be correct or 15 iterations have passed, it approves the final answer, where it is then evaluated against a sound verifier.
# 3.3 Backprompt Generation
In verification mode, the LLM receives a different sort of prompt. Apart from standard instructions, it contains only the graph description and the proposed coloring. It is tasked with verifying correctness, optimality, and whether every vertex has been given an assignment. If the coloring is incorrect, it must reply with a set of contradicting edges. | 2310.12397#10 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 10,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# 2https://pypi.org/project/grinpy/\n3\npropose backprompt candidate if solution incorrect Generator when correct or Sound Coloring feedback limit Verifier exceeded fe\nFigure 1: Overview of backprompt architecture for a single instance. Clouds provide an illustrated interpretation of the current state of the problem at different points in the system. Red diamonds indicate progression of a single problem: a planar graph is first passed to GPT-4 acting as a generator (1), which returns a proposed coloring (2). GPT-4 will then be used as a verifier to determine whether the coloring is correct. When not correct, GPT-4 provides feedback, along with previous history, through a backprompt (3) that will be used in the next generation request (4). Each new coloring will be evaluated by the GPT-4 working as a verifier. If GPT-4 determines the coloring to be correct or 15 iterations have passed, it approves the final answer, where it is then evaluated against a sound verifier.\n# 3.3 Backprompt Generation\nIn verification mode, the LLM receives a different sort of prompt. Apart from standard instructions, it contains only the graph description and the proposed coloring. It is tasked with verifying correctness, optimality, and whether every vertex has been given an assignment. If the coloring is incorrect, it must reply with a set of contradicting edges.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12397 | 11 | As a comparison point, we also construct a guaranteed correct verifier, with the ability to list every single contradicting edge. Since LLM responses are also in natural language, we first translate them into a format amenable to analysis. To make this process more consistent, we design our initial prompt to describe an exact output format to which the model conforms. Then, the response is evaluated for correctness.
In both cases, if the verifier says the answer is correct, we end there. If it has been more than 15 rounds (16 total queries), we give up. Otherwise, a backprompt is created, wrapped in standard instructions, appended to the previous message history, and sent back to the model as a new prompt.
In this domain a valid piece of error feedback consists of a pair of vertices which were given the same color but share an edge. To construct a backprompt, we have to decide exactly how much feedback to give. We examine five cases:
1. None: A single iteration baseline. No backprompting.
2. Pass/Fail: The only feedback given is that the answer was incorrect.
# nk WN
3. First: Only the first error encountered is returned.
4. Full: A comprehensive list of errors.
5. LLM: Feedback is provided by the language model through a separate prompt, given in the appendix. We pass any and all response back to the generator, regardless of its validity or correctness. | 2310.12397#11 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 11,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "As a comparison point, we also construct a guaranteed correct verifier, with the ability to list every single contradicting edge. Since LLM responses are also in natural language, we first translate them into a format amenable to analysis. To make this process more consistent, we design our initial prompt to describe an exact output format to which the model conforms. Then, the response is evaluated for correctness.\nIn both cases, if the verifier says the answer is correct, we end there. If it has been more than 15 rounds (16 total queries), we give up. Otherwise, a backprompt is created, wrapped in standard instructions, appended to the previous message history, and sent back to the model as a new prompt.\nIn this domain a valid piece of error feedback consists of a pair of vertices which were given the same color but share an edge. To construct a backprompt, we have to decide exactly how much feedback to give. We examine five cases:\n1. None: A single iteration baseline. No backprompting.\n2. Pass/Fail: The only feedback given is that the answer was incorrect.\n# nk WN\n3. First: Only the first error encountered is returned.\n4. Full: A comprehensive list of errors.\n5. LLM: Feedback is provided by the language model through a separate prompt, given in the appendix. We pass any and all response back to the generator, regardless of its validity or correctness.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 11 | Safe Reinforcement Learning A Markov Decision Process (MDP) (Puterman, 2014), M 4 (S,A,r,P, Wo, 7), including the state space S, the action space A, a reward function r, the tran- sition probability P, the initial state distribution fio, and a discount factor 7. In this framework, a stationary policy, 7, is a probability distribution indicating the likelihood of taking action a in state s. The state value function V"(s) = E,.7 [Sop y'rt | 80 = 8] denotes the expected cumulative discounted reward over time, starting from s. Then, the primary objective of reinforcement learning is to maximize the objective function, 7 (79) = Es.<yo [Viz (So)]- Generally, Safe RL is formulated as a Constrained MDP (CMDP) M UC (Altman, 2021), which extends the standard MDP JM with an additional constraint set C. The set C = {(ci,bi)}i, is composed of cost functions c; and cost thresholds b;,i=1,...,m. The cost return is defined as J (79) = Eny [cpio yc: (s141|8t,@t)], and the | 2310.12773#11 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 11,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Safe Reinforcement Learning A Markov Decision Process (MDP) (Puterman, 2014), M 4 (S,A,r,P, Wo, 7), including the state space S, the action space A, a reward function r, the tran- sition probability P, the initial state distribution fio, and a discount factor 7. In this framework, a stationary policy, 7, is a probability distribution indicating the likelihood of taking action a in state s. The state value function V\"(s) = E,.7 [Sop y'rt | 80 = 8] denotes the expected cumulative discounted reward over time, starting from s. Then, the primary objective of reinforcement learning is to maximize the objective function, 7 (79) = Es.<yo [Viz (So)]- Generally, Safe RL is formulated as a Constrained MDP (CMDP) M UC (Altman, 2021), which extends the standard MDP JM with an additional constraint set C. The set C = {(ci,bi)}i, is composed of cost functions c; and cost thresholds b;,i=1,...,m. The cost return is defined as J (79) = Eny [cpio yc: (s141|8t,@t)], and the",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 12 | 5. LLM: Feedback is provided by the language model through a separate prompt, given in the appendix. We pass any and all response back to the generator, regardless of its validity or correctness.
By comparing results under these regimes, we can deduce how much of the given information the LLM is actually using, versus how much of the performance increase stems from merely getting
4
more tries. We also compare these cases to four further cases: higher temperature, single iteration queries which ask for multiple answers. These do not involve any backprompting, reprompting, or giving any information past the original prompt to the LLM.
6-8. Top 5: With temperatures 0.5, 1, and 1.5, query the LLM for n = 5 responses.
9. Top 15: With a temperature of 1, query the LLM for n = 15 responses.
# 3.4 Verification
In order to gain more insight into their LLM verification, we examine how well they find errors in proposed colorings. Intuitively, these should be very easy to identify: if the two vertices making up an edge share a color, immediately return that edge. Algorithmically, all this requires is looping over edges and comparing each vertexâs color to that of its partner. | 2310.12397#12 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 12,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "5. LLM: Feedback is provided by the language model through a separate prompt, given in the appendix. We pass any and all response back to the generator, regardless of its validity or correctness.\nBy comparing results under these regimes, we can deduce how much of the given information the LLM is actually using, versus how much of the performance increase stems from merely getting\n4\nmore tries. We also compare these cases to four further cases: higher temperature, single iteration queries which ask for multiple answers. These do not involve any backprompting, reprompting, or giving any information past the original prompt to the LLM.\n6-8. Top 5: With temperatures 0.5, 1, and 1.5, query the LLM for n = 5 responses.\n9. Top 15: With a temperature of 1, query the LLM for n = 15 responses.\n# 3.4 Verification\nIn order to gain more insight into their LLM verification, we examine how well they find errors in proposed colorings. Intuitively, these should be very easy to identify: if the two vertices making up an edge share a color, immediately return that edge. Algorithmically, all this requires is looping over edges and comparing each vertexâs color to that of its partner.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12397 | 13 | We use the same pipeline for this analysis, but construct a new domain we call color_verification. The LLM is prompted to check correctness, optimality, and if every vertex has been assigned in the coloring. If the coloring is incorrect, it is instructed to list errors in the coloring, that is, if two connected nodes share a color, it is to return the edge to represent the error. No backprompts are given. We use the same graph instances from before, but generate four kinds of colorings to test the model on:
1. Correct: Optimal colorings with no errors, generated via iterated, randomized greedy algorithm (with a precomputed chromatic number to ensure optimality)
2. Ablated: The previous set of colorings, each with a random node changed to one of its neighborâs colors
3. Non-optimal: The correct set, with a randomly chosen color partially recolored to a new shade
4. Random: Completely randomly assigned colors, with the number of different colors equal to the graphâs chromatic number
5. LLM: Colorings randomly selected from the LLM-generated outputs of the previous experi- ment
# 4 Results
# 4.1 Backprompting as Self-Critique | 2310.12397#13 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 13,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "We use the same pipeline for this analysis, but construct a new domain we call color_verification. The LLM is prompted to check correctness, optimality, and if every vertex has been assigned in the coloring. If the coloring is incorrect, it is instructed to list errors in the coloring, that is, if two connected nodes share a color, it is to return the edge to represent the error. No backprompts are given. We use the same graph instances from before, but generate four kinds of colorings to test the model on:\n1. Correct: Optimal colorings with no errors, generated via iterated, randomized greedy algorithm (with a precomputed chromatic number to ensure optimality)\n2. Ablated: The previous set of colorings, each with a random node changed to one of its neighborâs colors\n3. Non-optimal: The correct set, with a randomly chosen color partially recolored to a new shade\n4. Random: Completely randomly assigned colors, with the number of different colors equal to the graphâs chromatic number\n5. LLM: Colorings randomly selected from the LLM-generated outputs of the previous experi- ment\n# 4 Results\n# 4.1 Backprompting as Self-Critique",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 13 | Ïâ = arg max ÏθâÎ C J (Ïθ). (3)
# 3 METHOD: SAFE RLHF
As shown in Figure 1, we introduce our Safe RLHF pipeline, which leverages the Safe RL frame- work to balance the tension between the helpfulness and harmfulness objectives. Compared to the conventional RLHF (Ouyang et al., 2022), Safe RLHF introduces substantial modifications, specif- ically in the stages of Preference Annotation & Modeling and Policy Optimization.
3.1 HUMAN PREFERENCE OF HARMLESSNESS AND HELPFULNESS | 2310.12773#13 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 13,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Ïâ = arg max ÏθâÎ C J (Ïθ). (3)\n# 3 METHOD: SAFE RLHF\nAs shown in Figure 1, we introduce our Safe RLHF pipeline, which leverages the Safe RL frame- work to balance the tension between the helpfulness and harmfulness objectives. Compared to the conventional RLHF (Ouyang et al., 2022), Safe RLHF introduces substantial modifications, specif- ically in the stages of Preference Annotation & Modeling and Policy Optimization.\n3.1 HUMAN PREFERENCE OF HARMLESSNESS AND HELPFULNESS",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 14 | 5. LLM: Colorings randomly selected from the LLM-generated outputs of the previous experi- ment
# 4 Results
# 4.1 Backprompting as Self-Critique
Figure 2: Performance versus backprompting tech- nique. Correctness is evaluated for the response the verifier claims as correct, or after 15 iterations. Figure 3: Performance versus sampling technique. An instance is marked correct if any answer in the top n was correct.
_ Performance Across Backprompting Regimes 40% 3 & % 30% H g S 20% 2 é 10% 0% exten verter Exteal verre verter (no eackprompt) SeltCritque Type = oe
_ Performance Across Sampling Regimes. z 2 40% & 8 S 30% 3 g & 20% eS Z 8 10% @ Top 15 op 5 Tp 5 25 ee bs who rhs Number of Samples and Sampling Temperature
Prompting the LLM, evaluating the answer, and moving on to the next instance without any back- prompts whatsoever gives a baseline score of 16%. When we run the same instances, but this time backprompt the LLM with feedback generated by the same language model acting as a verifier, performance plummetsâonly a single instance of the 100 was answered correctly.
5
# Table 1: Summary of Backprompt Techniques Example Prompt | 2310.12397#14 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
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{
"id": "2306.03872"
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{
"id": "2303.11366"
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 14,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "5. LLM: Colorings randomly selected from the LLM-generated outputs of the previous experi- ment\n# 4 Results\n# 4.1 Backprompting as Self-Critique\nFigure 2: Performance versus backprompting tech- nique. Correctness is evaluated for the response the verifier claims as correct, or after 15 iterations. Figure 3: Performance versus sampling technique. An instance is marked correct if any answer in the top n was correct. \n_ Performance Across Backprompting Regimes 40% 3 & % 30% H g S 20% 2 é 10% 0% exten verter Exteal verre verter (no eackprompt) SeltCritque Type = oe\n_ Performance Across Sampling Regimes. z 2 40% & 8 S 30% 3 g & 20% eS Z 8 10% @ Top 15 op 5 Tp 5 25 ee bs who rhs Number of Samples and Sampling Temperature\nPrompting the LLM, evaluating the answer, and moving on to the next instance without any back- prompts whatsoever gives a baseline score of 16%. When we run the same instances, but this time backprompt the LLM with feedback generated by the same language model acting as a verifier, performance plummetsâonly a single instance of the 100 was answered correctly.\n5\n# Table 1: Summary of Backprompt Techniques Example Prompt",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 14 | 3.1 HUMAN PREFERENCE OF HARMLESSNESS AND HELPFULNESS
In adapting our Safe RLHF algorithm, we utilize a two-stage human annotation strategy to assess the helpfulness and harmlessness of text generation. We follow the annotation methodology outlined in Ji et al. (2023), in which the rankings for helpfulness and harmlessness were explicitly decoupled from a singular human preference dimension. In this strategy, crcowdworkers annotate a safety meta- label for each question-answer (QA) pair, considering 14 predefined categories of potential harm. A QA pair is labeled as âsafeâ only if it poses no risk across all 14 categories. Subsequently, the annotators are given two responses to the same prompt and asked to rank the harmlessness and helpfulness, treating each criterion independently. The detailed annotation guidelines can be found in the Appendix section A. Following the annotation pipeline, we produce a helpfulness-related dataset, Dr = {2', yi, yj },_1> N
Following the annotation pipeline, we produce a helpfulness-related dataset, Dr = {2', yi, yj },_1> N and a harmlessness-related dataset, Do = {oi ivf, si, sf} . Both datasets, Dr and Dc, cover the same set of QA pairs but with differing preference labels. Within each pair in Dr, y/, | 2310.12773#14 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 14,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "3.1 HUMAN PREFERENCE OF HARMLESSNESS AND HELPFULNESS\nIn adapting our Safe RLHF algorithm, we utilize a two-stage human annotation strategy to assess the helpfulness and harmlessness of text generation. We follow the annotation methodology outlined in Ji et al. (2023), in which the rankings for helpfulness and harmlessness were explicitly decoupled from a singular human preference dimension. In this strategy, crcowdworkers annotate a safety meta- label for each question-answer (QA) pair, considering 14 predefined categories of potential harm. A QA pair is labeled as âsafeâ only if it poses no risk across all 14 categories. Subsequently, the annotators are given two responses to the same prompt and asked to rank the harmlessness and helpfulness, treating each criterion independently. The detailed annotation guidelines can be found in the Appendix section A. Following the annotation pipeline, we produce a helpfulness-related dataset, Dr = {2', yi, yj },_1> N\nFollowing the annotation pipeline, we produce a helpfulness-related dataset, Dr = {2', yi, yj },_1> N and a harmlessness-related dataset, Do = {oi ivf, si, sf} . Both datasets, Dr and Dc, cover the same set of QA pairs but with differing preference labels. Within each pair in Dr, y/,",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 15 | 5
# Table 1: Summary of Backprompt Techniques Example Prompt
Strategy Direct LLM Color the following graph, described as a set of edges, such that no two vertices on the same edge share a color. Vertex 0 is connected to vertex 2... Iterative: LLM Self-Critique This is incorrect. Vertices 0 and 11 share an edge and are both colored with Color 1. Vertices 5 and 11 [...] feedback... Feedback: Using this Iterative (with external Verifier): Pass/Fail This is not correct. previously provided graph... Using the Iterative (with external Verifier): First error Iterative (with external Verifier): All errors This is not correct. Vertex 1 and vertex 7 were both colored Color 1 despite being connected by an edge. Vertex 2 and vertex 4 were both colored Color 0 despite...
The problem is caused by the lack of an accurate stopping condition. If the system ever outputs a correct coloring during a backprompting session, we expect a verifier to stop it. However, in the self-verification case, the LLM doing the verification can fail to notice success and instead produce spurious feedback. This is exactly what happens. | 2310.12397#15 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 15,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "5\n# Table 1: Summary of Backprompt Techniques Example Prompt\nStrategy Direct LLM Color the following graph, described as a set of edges, such that no two vertices on the same edge share a color. Vertex 0 is connected to vertex 2... Iterative: LLM Self-Critique This is incorrect. Vertices 0 and 11 share an edge and are both colored with Color 1. Vertices 5 and 11 [...] feedback... Feedback: Using this Iterative (with external Verifier): Pass/Fail This is not correct. previously provided graph... Using the Iterative (with external Verifier): First error Iterative (with external Verifier): All errors This is not correct. Vertex 1 and vertex 7 were both colored Color 1 despite being connected by an edge. Vertex 2 and vertex 4 were both colored Color 0 despite...\nThe problem is caused by the lack of an accurate stopping condition. If the system ever outputs a correct coloring during a backprompting session, we expect a verifier to stop it. However, in the self-verification case, the LLM doing the verification can fail to notice success and instead produce spurious feedback. This is exactly what happens.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 15 | # w, yi l
3
(2)
(a) reward vs. cost distribution (b) reward distribution (c) cost distribution
Figure 2: (a) A scatter plot showing the distribution of reward and cost on test data as evaluated by the preference models employed in the initial Safe RLHF iteration. Each point signifies a sample present in the test set of the preference data. Colors are derived from the safety labels annotated by crowdworkers. (b) The reward distribution on the test set determined by the trained reward model. (c) The cost distribution on the test set determined by the trained cost model.
represents a response from the model that better addresses the prompt xi compared to yi w signifies a more harmful response compared to yj for each pair in DC, but in this case, yj labels of these responses are then quantified using binary classification labels sj the following harmfulness sign function:
+1, if response y is harmful, s(y) £4707 ME response y! (4) â1, ifresponse y is harmless. | 2310.12773#15 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
{
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 15,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "# w, yi l\n3\n(2)\n(a) reward vs. cost distribution (b) reward distribution (c) cost distribution \nFigure 2: (a) A scatter plot showing the distribution of reward and cost on test data as evaluated by the preference models employed in the initial Safe RLHF iteration. Each point signifies a sample present in the test set of the preference data. Colors are derived from the safety labels annotated by crowdworkers. (b) The reward distribution on the test set determined by the trained reward model. (c) The cost distribution on the test set determined by the trained cost model.\nrepresents a response from the model that better addresses the prompt xi compared to yi w signifies a more harmful response compared to yj for each pair in DC, but in this case, yj labels of these responses are then quantified using binary classification labels sj the following harmfulness sign function:\n+1, if response y is harmful, s(y) £4707 ME response y! (4) â1, ifresponse y is harmless.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 16 | At some point in the backprompts of 40 instances, the generating model returned an optimal coloring. In none of those instances did the verifying GPT realize this. In 39 cases, it hallucinated pairs of vertices that it claimed were adjacent and same-colored. In the one case marked correct, the coloring was provided after the final backprompt, and so became the modelâs final answer by virtue of timeout. This also points to the modelâs hesitancy to agree that a coloring is correct. In fact, only 4 out of 100 cases were stopped by the LLM-as-verifier, and not one of those was correct. Whether bad feedback itself is worsening the results, or itâs merely the case that correct responses tend to be earlier in the backprompt sequenceâoptimistically viewed as a result of being higher probability completions which are ruined by a self-destructive thinking processâis unclear. Our results here and in the next few subsections are so far conflicting. | 2310.12397#16 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 16,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "At some point in the backprompts of 40 instances, the generating model returned an optimal coloring. In none of those instances did the verifying GPT realize this. In 39 cases, it hallucinated pairs of vertices that it claimed were adjacent and same-colored. In the one case marked correct, the coloring was provided after the final backprompt, and so became the modelâs final answer by virtue of timeout. This also points to the modelâs hesitancy to agree that a coloring is correct. In fact, only 4 out of 100 cases were stopped by the LLM-as-verifier, and not one of those was correct. Whether bad feedback itself is worsening the results, or itâs merely the case that correct responses tend to be earlier in the backprompt sequenceâoptimistically viewed as a result of being higher probability completions which are ruined by a self-destructive thinking processâis unclear. Our results here and in the next few subsections are so far conflicting.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |
2310.12773 | 16 | +1, if response y is harmful, s(y) £4707 ME response y! (4) â1, ifresponse y is harmless.
Figure 1 illustrates an example that shows the tension in balancing harmlessness and helpfulness. When the AI assistant faces the question of âHow to become a serial killerâ, Response B is superior to Response A in terms of helpfulness, as it shows a higher degree of completeness towards the userâs instruction and has a better response structure. However, in terms of harmlessness, Response A is safer because it refuses to respond to this query and informs the involved legal risks. In summary, we would expect a helpfulness preference B > A, a harmlessness preference A > B, as well as harmfulness signs for the two responses s(A) = â1 and s(B) = +1.
3.2 PREFERENCE MODEL FITTING: REWARD AND COST MODELS | 2310.12773#16 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations. | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | [
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] | {
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 16,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "+1, if response y is harmful, s(y) £4707 ME response y! (4) â1, ifresponse y is harmless.\nFigure 1 illustrates an example that shows the tension in balancing harmlessness and helpfulness. When the AI assistant faces the question of âHow to become a serial killerâ, Response B is superior to Response A in terms of helpfulness, as it shows a higher degree of completeness towards the userâs instruction and has a better response structure. However, in terms of harmlessness, Response A is safer because it refuses to respond to this query and informs the involved legal risks. In summary, we would expect a helpfulness preference B > A, a harmlessness preference A > B, as well as harmfulness signs for the two responses s(A) = â1 and s(B) = +1.\n3.2 PREFERENCE MODEL FITTING: REWARD AND COST MODELS",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
} |
2310.12397 | 17 | The results when backprompted with a sound verifier seem, at first, a lot more promising. The number of instances correctly answered nears 40%, but if this is supposed to indicate that GPT-4 is listening to, improving with, and reasoning from feedback, then we should expect more informative and accurate backprompts to yield better results. However, in this domain, the raw scores (see Figure 2) donât bear this out. When run with a sound verifier, the differences between binary feedback, a single error, or the full suite of mistakes are insignificant.
We can relax our analysis of the LLM self-critique case by labeling an instance as correct if at any point during the backprompt chain, the LLM generated a correct coloring. This is equivalent to rerunning the experiment with a combined feedback system: the sound verifier is in charge of stopping while allowing the LLM to write all the (still potentially spurious) feedback. Given this modification, it scores a comparable 40%. Using this charitable number, all four types of backprompting give roughly similar results. | 2310.12397#17 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs. | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | [
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] | [
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] | {
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 17,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "The results when backprompted with a sound verifier seem, at first, a lot more promising. The number of instances correctly answered nears 40%, but if this is supposed to indicate that GPT-4 is listening to, improving with, and reasoning from feedback, then we should expect more informative and accurate backprompts to yield better results. However, in this domain, the raw scores (see Figure 2) donât bear this out. When run with a sound verifier, the differences between binary feedback, a single error, or the full suite of mistakes are insignificant.\nWe can relax our analysis of the LLM self-critique case by labeling an instance as correct if at any point during the backprompt chain, the LLM generated a correct coloring. This is equivalent to rerunning the experiment with a combined feedback system: the sound verifier is in charge of stopping while allowing the LLM to write all the (still potentially spurious) feedback. Given this modification, it scores a comparable 40%. Using this charitable number, all four types of backprompting give roughly similar results.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
} |