GenAI4ELab/papercli-papers huggingface dataset:
Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness
Authors: Shixuan Ma; Quan Wang
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of LLM-generated text. Zero-shot detectors, due to their training-free nature, have received considerable attention and notable success. In this paper, we identify a new feature, token cohesiveness, that is useful for zero-shot detection, and we demonstrate that LLM-generated text tends to exhibit higher token cohesiveness than human-written text. Based on this observation, we devise TOCSIN, a generic dual-channel detection paradigm that uses token cohesiveness as a plug-and-play module to improve existing zero-shot detectors. To calculate token cohesiveness, TOCSIN only requires a few rounds of random token deletion and semantic difference measurement, making it particularly suitable for a practical black-box setting where the source model used for generation is not accessible. Extensive experiments with four state-of-the-art base detectors on various datasets, source models, and evaluation settings demonstrate the effectiveness and generality of the proposed approach. Code available at: https://github.com/Shixuan-Ma/TOCSIN.
Dual-oriented Disentangled Network with Counterfactual Intervention for Multimodal Intent Detection
Authors: Zhanpeng Chen; Zhihong Zhu; Xianwei Zhuang; Zhiqi Huang; Yuexian Zou
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Multimodal intent detection is designed to leverage diverse modalities for a comprehensive understanding of user intentions in real-world scenarios, thus playing a critical role in modern task-oriented dialogue systems. Existing methods have made great progress in modal alignment and fusion, however, two vital limitations are neglected: (I) close entanglement of multimodal semantics with modal structures; (II) insufficient learning of the causal effects of semantic and modality-specific information on the final predictions under the end-to-end training fashion. To alleviate the above limitations, we introduce the Dual-oriented Disentangled Network with Counterfactual Intervention (DuoDN). DuoDN addresses key limitations in current systems by effectively disentangling and utilizing modality-specific and multimodal semantic information. The model consists of a Dual-oriented Disentangled Encoder that decouples semantics-oriented and modality-oriented representations, alongside a Counterfactual Intervention Module that applies causal inference to understand causal effects by injecting confounders. Experiments on three benchmark datasets demonstrate DuoDN’s superiority over existing methods, with extensive analysis validating its advantages.
From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking
Authors: Siyuan Wang; Zhuohan Long; Zhihao Fan; Zhongyu Wei
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: The rapid development of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has exposed vulnerabilities to various adversarial attacks. This paper provides a comprehensive overview of jailbreaking research targeting both LLMs and MLLMs, highlighting recent advancements in evaluation benchmarks, attack techniques and defense strategies. Compared to the more advanced state of unimodal jailbreaking, multimodal domain remains underexplored. We summarize the limitations and potential research directions of multimodal jailbreaking, aiming to inspire future research and further enhance the robustness and security of MLLMs.
Symbolic Working Memory Enhances Language Models for Complex Rule Application
Authors: Siyuan Wang; Zhongyu Wei; Yejin Choi; Xiang Ren
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) have shown remarkable reasoning performance but struggle with multi-step deductive reasoning involving a series of rule application steps, especially when rules are presented non-sequentially. Our preliminary analysis shows that while LLMs excel in single-step rule application, their performance drops significantly in multi-step scenarios due to the challenge in rule grounding. It requires anchoring the applicable rule and supporting facts at each step, amidst multiple input rules, facts, and inferred facts. To address this, we propose augmenting LLMs with external working memory and introduce a neurosymbolic framework for rule application. The memory stores facts and rules in both natural language and symbolic forms, enabling precise tracking. Utilizing this memory, our framework iteratively performs symbolic rule grounding and LLM-based rule implementation. The former matches predicates and variables of symbolic rules and facts to ground applicable rules at each step. Experiments indicate our framework’s effectiveness in rule application and its robustness across various steps and settings.
LLoCO: Learning Long Contexts Offline
Authors: Sijun Tan; Xiuyu Li; Shishir G Patil; Ziyang Wu; Tianjun Zhang; Kurt Keutzer; Joseph E. Gonzalez; Raluca Ada Popa
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Processing long contexts remains a challenge for large language models (LLMs) due to the quadratic computational and memory overhead of the self-attention mechanism and the substantial KV cache sizes during generation. We propose LLoCO, a novel approach to address this problem by learning contexts offline through context compression and in-domain parameter-efficient finetuning with LoRA. Our method enables an LLM to create a concise representation of the original context and efficiently retrieve relevant information to answer questions accurately. Our approach extends the effective context window of a 4k token LLaMA2-7B model to handle up to 128k tokens. We evaluate our approach on several long-context question-answering datasets, demonstrating that LLoCO significantly outperforms in-context learning while using 30 × fewer tokens during inference. LLoCO achieves up to 7.62 × speed-up during inference and 11.52 × higher throughput during finetuning, substantially reduces the cost of long document question answering. This makes it a promising solution for efficient long context processing.
Don’t Forget Your Reward Values: Language Model Alignment via Value-based Calibration
Authors: Xin Mao; Feng-Lin Li; Huimin Xu; Wei Zhang; Wang Chen; Anh Tuan Luu
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding the complexity and instability associated with the Proximal Policy Optimization (PPO) algorithm, proposing a series of order-based alignment methods as viable alternatives. This paper delves into existing order-based methods, unifying them into one framework and examining their inefficiencies in utilizing reward values. Building upon these findings, we propose a new Value-based Calibration (VCB) method to better align LLMs with human preferences. Experimental results demonstrate that VCB surpasses existing alignment methods on AI assistant and summarization datasets, providing impressive generalizability, robustness, and diversity in different settings.
Mentor-KD: Making Small Language Models Better Multi-step Reasoners
Authors: Hojae Lee; Junho Kim; SangKeun Lee
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) have displayed remarkable performances across various complex tasks by leveraging Chain-of-Thought (CoT) prompting. Recently, studies have proposed a Knowledge Distillation (KD) approach, reasoning distillation, which transfers such reasoning ability of LLMs through fine-tuning language models of multi-step rationales generated by LLM teachers. However, they have inadequately considered two challenges regarding insufficient distillation sets from the LLM teacher model, in terms of 1) data quality and 2) soft label provision. In this paper, we propose Mentor-KD, which effectively distills the multi-step reasoning capability of LLMs to smaller LMs while addressing the aforementioned challenges. Specifically, we exploit a mentor, intermediate-sized task-specific fine-tuned model, to augment additional CoT annotations and provide soft labels for the student model during reasoning distillation. We conduct extensive experiments and confirm Mentor-KD’s effectiveness across various models and complex reasoning tasks.
Are Large Language Models Capable of Generating Human-Level Narratives?
Authors: Yufei Tian; Tenghao Huang; Miri Liu; Derek Jiang; Alexander Spangher; Muhao Chen; Jonathan May; Nanyun Peng
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: As daily reliance on large language models (LLMs) grows, assessing their generation quality is crucial to understanding how they might impact on our communications. This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression. We introduce a novel computational framework to analyze narratives through three discourse-level aspects: i) story arcs, ii) turning points, and iii) affective dimensions, including arousal and valence. By leveraging expert and automatic annotations, we uncover significant discrepancies between the LLM- and human- written stories. While human-written stories are suspenseful, arousing, and diverse in narrative structures, LLM stories are homogeneously positive and lack tension. Next, we measure narrative reasoning skills as a precursor to generative capacities, concluding that most LLMs fall short of human abilities in discourse understanding. Finally, we show that explicit integration of aforementioned discourse features can enhance storytelling, as is demonstrated by over 40% improvement in neural storytelling in terms of diversity, suspense, and arousal. Such advances promise to facilitate greater and more natural roles LLMs in human communication.
MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs
Authors: Yerin Hwang; Yongil Kim; Yunah Jang; Jeesoo Bang; Hyunkyung Bae; Kyomin Jung
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets. To address this issue, we propose Multi-Passage to Dialogue (MP2D), a data generation framework that automatically creates conversational question-answering datasets with natural topic transitions. By leveraging the relationships between entities in a knowledge graph, MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation. It retrieves relevant passages corresponding to the topics and transforms them into dialogues through the passage-to-dialogue method. Through quantitative and qualitative experiments, we demonstrate MP2D’s efficacy in generating dialogue with natural topic shifts. Furthermore, this study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog. Utilizing the dataset, we demonstrate that even Large Language Models (LLMs) struggle to handle topic shifts in dialogue effectively, and we showcase the performance improvements of models trained on datasets generated by MP2D across diverse topic shift dialogue tasks.
Can Large Language Models Enhance Predictions of Disease Progression? Investigating Through Disease Network Link Prediction
Authors: Haohui Lu; Usman Naseem
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) have made significant strides in various tasks, yet their effectiveness in predicting disease progression remains relatively unexplored. To fill this gap, we use LLMs and employ advanced graph prompting and Retrieval-Augmented Generation (RAG) to predict disease comorbidity within disease networks. Specifically, we introduce a disease Comorbidity prediction model using LLM, named ComLLM, which leverages domain knowledge to enhance the prediction performance. Based on the comprehensive experimental results, ComLLM consistently outperforms conventional models, such as Graph Neural Networks, achieving average area under the curve (AUC) improvements of 10.70% and 6.07% over the best baseline models in two distinct disease networks. ComLLM is evaluated across multiple settings for disease progression prediction, employing various prompting strategies, including zero-shot, few-shot, Chain-of-Thought, graph prompting and RAG. Our results show that graph prompting and RAG enhance LLM performance in disease progression prediction tasks. ComLLM exhibits superior predictive capabilities and serves as a proof-of-concept for LLM-based systems in disease progression prediction, highlighting its potential for broad applications in healthcare.
Searching for Best Practices in Retrieval-Augmented Generation
Authors: Xiaohua Wang; Zhenghua Wang; Xuan Gao; Feiran Zhang; Yixin Wu; Zhibo Xu; Tianyuan Shi; Zhengyuan Wang; Shizheng Li; Qi Qian; Ruicheng Yin; Changze Lv; Xiaoqing Zheng; Xuanjing Huang
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, particularly in specialized domains. While many RAG approaches have been proposed to enhance large language models through query-dependent retrievals, these approaches still suffer from their complex implementation and prolonged response times. Typically, a RAG workflow involves multiple processing steps, each of which can be executed in various ways. Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices. Through extensive experiments, we suggest several strategies for deploying RAG that balance both performance and efficiency. Moreover, we demonstrate that multimodal retrieval techniques can significantly enhance question-answering capabilities about visual inputs and accelerate the generation of multimodal content using a “retrieval as generation” strategy.
Moral Foundations of Large Language Models
Authors: Marwa Abdulhai; Gregory Serapio-García; Clement Crepy; Daria Valter; John Canny; Natasha Jaques
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Moral foundations theory (MFT) is a psychological assessment tool that decomposes human moral reasoning into five factors, including care/harm, liberty/oppression, and sanctity/degradation (Graham et al., 2009). People vary in the weight they place on these dimensions when making moral decisions, in part due to their cultural upbringing and political ideology. As large language models (LLMs) are trained on datasets collected from the internet, they may reflect the biases that are present in such corpora. This paper uses MFT as a lens to analyze whether popular LLMs have acquired a bias towards a particular set of moral values. We analyze known LLMs and find they exhibit particular moral foundations, and show how these relate to human moral foundations and political affiliations. We also measure the consistency of these biases, or whether they vary strongly depending on the context of how the model is prompted. Finally, we show that we can adversarially select prompts that encourage the moral to exhibit a particular set of moral foundations, and that this can affect the model’s behavior on downstream tasks. These findings help illustrate the potential risks and unintended consequences of LLMs assuming a particular moral stance.
The Zeno’s Paradox of ‘Low-Resource’ Languages
Authors: Hellina Hailu Nigatu; Atnafu Lambebo Tonja; Benjamin Rosman; Thamar Solorio; Monojit Choudhury
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a ‘low-resource language.’ To understand how NLP papers define and study ‘low resource’ languages, we qualitatively analyzed 150 papers from the ACL Anthology and popular speech-processing conferences that mention the keyword ‘low-resource.’ Based on our analysis, we show how several interacting axes contribute to ‘low-resourcedness’ of a language and why that makes it difficult to track progress for each individual language. We hope our work (1) elicits explicit definitions of the terminology when it is used in papers and (2) provides grounding for the different axes to consider when connoting a language as low-resource.
Knowledge Planning in Large Language Models for Domain-Aligned Counseling Summarization
Authors: Aseem Srivastava; Smriti Joshi; Tanmoy Chakraborty; Md Shad Akhtar
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: In mental health counseling, condensing dialogues into concise and relevant summaries (aka counseling notes) holds pivotal significance. Large Language Models (LLMs) exhibit remarkable capabilities in various generative tasks; however, their adaptation to domain-specific intricacies remains challenging, especially within mental health contexts. Unlike standard LLMs, mental health experts first plan to apply domain knowledge in writing summaries. Our work enhances LLMs’ ability by introducing a novel planning engine to orchestrate structuring knowledge alignment. To achieve high-order planning, we divide knowledge encapsulation into two major phases: (i) holding dialogue structure and (ii) incorporating domain-specific knowledge. We employ a planning engine on Llama-2, resulting in a novel framework, PIECE. Our proposed system employs knowledge filtering-cum-scaffolding to encapsulate domain knowledge. Additionally, PIECE leverages sheaf convolution learning to enhance its understanding of the dialogue’s structural nuances. We compare PIECE with 14 baseline methods and observe a significant improvement across ROUGE and Bleurt scores. Further, expert evaluation and analyses validate the generation quality to be effective, sometimes even surpassing the gold standard. We further benchmark PIECE with other LLMs and report improvement, including Llama-2 (+2.72%), Mistral (+2.04%), and Zephyr (+1.59%), to justify the generalizability of the planning engine.
Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition
Authors: Pritika Ramu; Koustava Goswami; Apoorv Saxena; Balaji Vasan Srinivasan
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Accurately attributing answer text to its source document is crucial for developing a reliable question-answering system. However, attribution for long documents remains largely unexplored. Post-hoc attribution systems are designed to map answer text back to the source document, yet the granularity of this mapping has not been addressed. Furthermore, a critical question arises: What exactly should be attributed? This involves identifying the specific information units within an answer that require grounding. In this paper, we propose and investigate a novel approach to the factual decomposition of generated answers for attribution, employing template-based in-context learning. To accomplish this, we utilize the question and integrate negative sampling during few-shot in-context learning for decomposition. This approach enhances the semantic understanding of both abstractive and extractive answers. We examine the impact of answer decomposition by providing a thorough examination of various attribution approaches, ranging from retrieval-based techniques to LLM-based attributors.
From Descriptive Richness to Bias: Unveiling the Dark Side of Generative Image Caption Enrichment
Authors: Yusuke Hirota; Ryo Hachiuma; Chao-Han Huck Yang; Yuta Nakashima
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) have enhanced the capacity of vision-language models to caption visual text. This generative approach to image caption enrichment further makes textual captions more descriptive, improving alignment with the visual context. However, while many studies focus on the benefits of generative caption enrichment (GCE), are there any negative side effects? We compare standard-format captions and recent GCE processes from the perspectives of gender bias and hallucination, showing that enriched captions suffer from increased gender bias and hallucination. Furthermore, models trained on these enriched captions amplify gender bias by an average of 30.9% and increase hallucination by 59.5%. This study serves as a caution against the trend of making captions more descriptive.
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging
Authors: Deyuan Liu; Zhanyue Qin; Hairu Wang; Zhao Yang; Zecheng Wang; Fangying Rong; Qingbin Liu; Yanchao Hao; Bo Li; Xi Chen; Cunhang Fan; Zhao Lv; Dianhui Chu; Zhiying Tu; Dianbo Sui
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: While large language models (LLMs) excel in many domains, their complexity and scale challenge deployment in resource-limited environments. Current compression techniques, such as parameter pruning, often fail to effectively utilize the knowledge from pruned parameters. To address these challenges, we propose Manifold-Based Knowledge Alignment and Layer Merging Compression (MKA), a novel approach that uses manifold learning and the Information Bottleneck (IB) measure to merge similar layers, reducing model size while preserving essential performance. We evaluate MKA on multiple benchmark datasets and various LLMs. Our findings show that MKA not only preserves model performance but also achieves substantial compression ratios, outperforming traditional pruning methods. Moreover, when coupled with quantization, MKA delivers even greater compression. Specifically, on the MMLU dataset using the Llama3-8B model, MKA achieves a compression ratio of 43.75% with a minimal performance decrease of only 2.82%. The proposed MKA method offers a resource-efficient and performance-preserving model compression technique for LLMs. We make our code available at https://github.com/SempraETY/Pruning-via-Merging