pwc-archive/papers-with-abstracts huggingface dataset:
Measuring Copyright Risks of Large Language Model via Partial Information Probing
Authors: Weijie Zhao, Huajie Shao, Zhaozhuo Xu, Suzhen Duan, Denghui Zhang
Date: 2024-09-20 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Exploring the data sources used to train Large Language Models (LLMs) is a crucial direction in investigating potential copyright infringement by these models. While this approach can identify the possible use of copyrighted materials in training data, it does not directly measure infringing risks. Recent research has shifted towards testing whether LLMs can directly output copyrighted content. Addressing this direction, we investigate and assess LLMs' capacity to generate infringing content by providing them with partial information from copyrighted materials, and try to use iterative prompting to get LLMs to generate more infringing content. Specifically, we input a portion of a copyrighted text into LLMs, prompt them to complete it, and then analyze the overlap between the generated content and the original copyrighted material. Our findings demonstrate that LLMs can indeed generate content highly overlapping with copyrighted materials based on these partial inputs.
STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions
Authors: Robert Morabito, Sangmitra Madhusudan, Tyler McDonald, Ali Emami
Date: 2024-09-20 00:00:00
Tasks: Benchmarking, Sensitivity

Mitigating explicit and implicit biases in Large Language Models (LLMs) has become a critical focus in the field of natural language processing. However, many current methodologies evaluate scenarios in isolation, without considering the broader context or the spectrum of potential biases within each situation. To address this, we introduce the Sensitivity Testing on Offensive Progressions (STOP) dataset, which includes 450 offensive progressions containing 2,700 unique sentences of varying severity that progressively escalate from less to more explicitly offensive. Covering a broad spectrum of 9 demographics and 46 sub-demographics, STOP ensures inclusivity and comprehensive coverage. We evaluate several leading closed- and open-source models, including GPT-4, Mixtral, and Llama 3. Our findings reveal that even the best-performing models detect bias inconsistently, with success rates ranging from 19.3% to 69.8%. We also demonstrate how aligning models with human judgments on STOP can improve model answer rates on sensitive tasks such as BBQ, StereoSet, and CrowS-Pairs by up to 191%, while maintaining or even improving performance. STOP presents a novel framework for assessing the complex nature of biases in LLMs, which will enable more effective bias mitigation strategies and facilitates the creation of fairer language models.
Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs
Authors: Julia Watson, Sophia Lee, Barend Beekhuizen, Suzanne Stevenson
Date: 2024-09-20 00:00:00

We study language ideologies in text produced by LLMs through a case study on English gendered language reform (related to role nouns like congressperson/-woman/-man, and singular they). First, we find political bias: when asked to use language that is "correct" or "natural", LLMs use language most similarly to when asked to align with conservative (vs. progressive) values. This shows how LLMs' metalinguistic preferences can implicitly communicate the language ideologies of a particular political group, even in seemingly non-political contexts. Second, we find LLMs exhibit internal inconsistency: LLMs use gender-neutral variants more often when more explicit metalinguistic context is provided. This shows how the language ideologies expressed in text produced by LLMs can vary, which may be unexpected to users. We discuss the broader implications of these findings for value alignment.
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting
Authors: Zhepeng Wang, Runxue Bao, Yawen Wu, Jackson Taylor, Cao Xiao, Feng Zheng, Weiwen Jiang, Shangqian Gao, yanfu Zhang
Date: 2024-09-20 00:00:00
Tasks: Code Generation, Memorization, Question Answering, Text Generation

Pretrained large language models (LLMs) have revolutionized natural language processing (NLP) tasks such as summarization, question answering, and translation. However, LLMs pose significant security risks due to their tendency to memorize training data, leading to potential privacy breaches and copyright infringement. Accurate measurement of this memorization is essential to evaluate and mitigate these potential risks. However, previous attempts to characterize memorization are constrained by either using prefixes only or by prepending a constant soft prompt to the prefixes, which cannot react to changes in input. To address this challenge, we propose a novel method for estimating LLM memorization using dynamic, prefix-dependent soft prompts. Our approach involves training a transformer-based generator to produce soft prompts that adapt to changes in input, thereby enabling more accurate extraction of memorized data. Our method not only addresses the limitations of previous methods but also demonstrates superior performance in diverse experimental settings compared to state-of-the-art techniques. In particular, our method can achieve the maximum relative improvement of 112.75% and 32.26% over the vanilla baseline in terms of discoverable memorization rate for the text generation task and code generation task respectively.
Instruct-Tuning Pretrained Causal Language Models for Ancient Greek Papyrology and Epigraphy
Authors: Eric Cullhed
Date: 2024-09-20 00:00:00

This article presents an experiment in fine-tuning a pretrained causal language model (Meta's Llama 3.1 8B Instruct) to assist with restoring missing or illegible characters in ancient Greek inscriptions and documentary papyri. Utilizing a straightforward instruction-based approach and a 95%/5% train/test split, the papyrus restoration model achieved a character error rate (CER) of 14.9%, a top-1 accuracy of 73.5%, and a top-20 accuracy of 86.0% for sequences up to 10 characters. A model was also fine-tuned for geographic attribution, reaching a top-1 accuracy of 66.4% and a top-3 accuracy of 79.9%. In chronological attribution, it demonstrated an average deviation of 21.7 years from the actual terminus post/ante quem, with a median deviation of 0 years. For inscriptions, the restoration model achieved a CER of 20.5%, a top-1 accuracy of 63.7%, and a top-20 accuracy of 83.0% for sequences up to 10 characters. In geographic attribution, it attained a top-1 accuracy of 75.0% and a top-3 accuracy of 83.7%, while in dating, it had an average deviation of 37.1 years and a median deviation of 3 years from the actual date range. Benchmarked against the state-of-the-art model (Ithaca) on a shared test set and on recently edited inscriptions, the instruction-tuned models excelled in text restoration, while also offering the practical advantage of ignoring spaces during reconstruction, which aligns with the scriptio continua of ancient textual artifacts. However, their performance in geographic and chronological attribution was lower than Ithaca's. To evaluate the approach in a more even setup, the instruction model was retrained with an 80%/10%/10% train-validation-test split, and still outperformed Ithaca in text restoration. The results suggest that fine-tuning larger pretrained causal language models using instruction templates for emendations and conjectures to ancient texts holds promise.
"I Never Said That": A dataset, taxonomy and baselines on response clarity classification
Authors: Konstantinos Thomas, Giorgos Filandrianos, Maria Lymperaiou, Chrysoula Zerva, Giorgos Stamou
Date: 2024-09-20 00:00:00

Equivocation and ambiguity in public speech are well-studied discourse phenomena, especially in political science and analysis of political interviews. Inspired by the well-grounded theory on equivocation, we aim to resolve the closely related problem of response clarity in questions extracted from political interviews, leveraging the capabilities of Large Language Models (LLMs) and human expertise. To this end, we introduce a novel taxonomy that frames the task of detecting and classifying response clarity and a corresponding clarity classification dataset which consists of question-answer (QA) pairs drawn from political interviews and annotated accordingly. Our proposed two-level taxonomy addresses the clarity of a response in terms of the information provided for a given question (high-level) and also provides a fine-grained taxonomy of evasion techniques that relate to unclear, ambiguous responses (lower-level). We combine ChatGPT and human annotators to collect, validate and annotate discrete QA pairs from political interviews, to be used for our newly introduced response clarity task. We provide a detailed analysis and conduct several experiments with different model architectures, sizes and adaptation methods to gain insights and establish new baselines over the proposed dataset and task.
A Multi-LLM Debiasing Framework
Authors: Deonna M. Owens, Ryan A. Rossi, Sungchul Kim, Tong Yu, Franck Dernoncourt, Xiang Chen, Ruiyi Zhang, Jiuxiang Gu, Hanieh Deilamsalehy, Nedim Lipka
Date: 2024-09-20 00:00:00
Tasks: Data Augmentation, Human Detection

Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Despite significant advancements in bias mitigation techniques using data augmentation, zero-shot prompting, and model fine-tuning, biases continuously persist, including subtle biases that may elude human detection. Recent research has shown a growing interest in multi-LLM approaches, which have been demonstrated to be effective in improving the quality of reasoning and factuality in LLMs. Building on this approach, we propose a novel multi-LLM debiasing framework aimed at reducing bias in LLMs. Our work is the first to introduce and evaluate two distinct approaches within this framework for debiasing LLMs: a centralized method, where the conversation is facilitated by a single central LLM, and a decentralized method, where all models communicate directly. Our findings reveal that our multi-LLM framework significantly reduces bias in LLMs, outperforming the baseline method across several social groups.
Transfer Learning with Clinical Concept Embeddings from Large Language Models
Authors: Yuhe Gao, Runxue Bao, Yuelyu Ji, Yiming Sun, Chenxi Song, Jeffrey P. Ferraro, Ye Ye
Date: 2024-09-20 00:00:00
Tasks: Transfer Learning

Knowledge sharing is crucial in healthcare, especially when leveraging data from multiple clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer learning can facilitate cross-site knowledge transfer, but a major challenge is heterogeneity in clinical concepts across different sites. Large Language Models (LLMs) show significant potential of capturing the semantic meaning of clinical concepts and reducing heterogeneity. This study analyzed electronic health records from two large healthcare systems to assess the impact of semantic embeddings from LLMs on local, shared, and transfer learning models. Results indicate that domain-specific LLMs, such as Med-BERT, consistently outperform in local and direct transfer scenarios, while generic models like OpenAI embeddings require fine-tuning for optimal performance. However, excessive tuning of models with biomedical embeddings may reduce effectiveness, emphasizing the need for balance. This study highlights the importance of domain-specific embeddings and careful model tuning for effective knowledge transfer in healthcare.
LLM for Everyone: Representing the Underrepresented in Large Language Models
Authors: Samuel Cahyawijaya
Date: 2024-09-20 00:00:00
Tasks: In-Context Learning

Natural language processing (NLP) has witnessed a profound impact of large language models (LLMs) that excel in a multitude of tasks. However, the limitation of LLMs in multilingual settings, particularly in underrepresented languages, remains a significant hurdle. This thesis aims to bridge the gap in NLP research and development by focusing on underrepresented languages. A comprehensive evaluation of LLMs is conducted to assess their capabilities in these languages, revealing the challenges of multilingual and multicultural generalization. Addressing the multilingual generalization gap, this thesis proposes data-and-compute-efficient methods to mitigate the disparity in LLM ability in underrepresented languages, allowing better generalization on underrepresented languages without the loss of task generalization ability. The proposed solutions cover cross-lingual continual instruction tuning, retrieval-based cross-lingual in-context learning, and in-context query alignment. Furthermore, a novel method to measure cultural values alignment between LLMs operating in different languages is proposed, ensuring cultural sensitivity and inclusivity. These contributions aim to enhance the multilingual and multicultural alignment of LLMs in underrepresented languages, ultimately advancing the NLP field toward greater equality and inclusiveness.
Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology
Authors: Aidan Gilson, Xuguang Ai, Thilaka Arunachalam, Ziyou Chen, Ki Xiong Cheong, Amisha Dave, Cameron Duic, Mercy Kibe, Annette Kaminaka, Minali Prasad, Fares Siddig, Maxwell Singer, Wendy Wong, Qiao Jin, Tiarnan D. L. Keenan, Xia Hu, Emily Y. Chew, Zhiyong Lu, Hua Xu, Ron A. Adelman, Yih-Chung Tham, Qingyu Chen
Date: 2024-09-20 00:00:00
Tasks: Evidence Selection, Form, Question Answering, RAG

Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address this issue, few studies implemented and evaluated RAG in downstream domain-specific applications. We developed a RAG pipeline with 70,000 ophthalmology-specific documents that retrieve relevant documents to augment LLMs during inference time. In a case study on long-form consumer health questions, we systematically evaluated the responses including over 500 references of LLMs with and without RAG on 100 questions with 10 healthcare professionals. The evaluation focuses on factuality of evidence, selection and ranking of evidence, attribution of evidence, and answer accuracy and completeness. LLMs without RAG provided 252 references in total. Of which, 45.3% hallucinated, 34.1% consisted of minor errors, and 20.6% were correct. In contrast, LLMs with RAG significantly improved accuracy (54.5% being correct) and reduced error rates (18.8% with minor hallucinations and 26.7% with errors). 62.5% of the top 10 documents retrieved by RAG were selected as the top references in the LLM response, with an average ranking of 4.9. The use of RAG also improved evidence attribution (increasing from 1.85 to 2.49 on a 5-point scale, P<0.001), albeit with slight decreases in accuracy (from 3.52 to 3.23, P=0.03) and completeness (from 3.47 to 3.27, P=0.17). The results demonstrate that LLMs frequently exhibited hallucinated and erroneous evidence in the responses, raising concerns for downstream applications in the medical domain. RAG substantially reduced the proportion of such evidence but encountered challenges.
Target word activity detector: An approach to obtain ASR word boundaries without lexicon
Authors: Sunit Sivasankaran, Eric Sun, Jinyu Li, Yan Huang, Jing Pan
Date: 2024-09-20 00:00:00
Tasks: Word Alignment, Word Embeddings

Obtaining word timestamp information from end-to-end (E2E) ASR models remains challenging due to the lack of explicit time alignment during training. This issue is further complicated in multilingual models. Existing methods, either rely on lexicons or introduce additional tokens, leading to scalability issues and increased computational costs. In this work, we propose a new approach to estimate word boundaries without relying on lexicons. Our method leverages word embeddings from sub-word token units and a pretrained ASR model, requiring only word alignment information during training. Our proposed method can scale-up to any number of languages without incurring any additional cost. We validate our approach using a multilingual ASR model trained on five languages and demonstrate its effectiveness against a strong baseline.
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks
Authors: Sebastian Nehrdich, Oliver Hellwig, Kurt Keutzer
Date: 2024-09-20 00:00:00
Tasks: All, Dependency Parsing, Information Retrieval, Language Modelling, Lemmatization, Machine Translation, model, Optical Character Recognition (OCR)

Morphologically rich languages are notoriously challenging to process for downstream NLP applications. This paper presents a new pretrained language model, ByT5-Sanskrit, designed for NLP applications involving the morphologically rich language Sanskrit. We evaluate ByT5-Sanskrit on established Sanskrit word segmentation tasks, where it outperforms previous data-driven approaches by a considerable margin and matches the performance of the current best lexicon-based model. It is easier to deploy and more robust to data not covered by external linguistic resources. It also achieves new state-of-the-art results in Vedic Sanskrit dependency parsing and OCR post-correction tasks. Additionally, based on the Digital Corpus of Sanskrit, we introduce a novel multitask dataset for the joint training of Sanskrit word segmentation, lemmatization, and morphosyntactic tagging tasks. We fine-tune ByT5-Sanskrit on this dataset, creating a versatile multitask model for various downstream Sanskrit applications. We have used this model in Sanskrit linguistic annotation projects, in information retrieval setups, and as a preprocessing step in a Sanskrit machine translation pipeline. We also show that our approach yields new best scores for lemmatization and dependency parsing of other morphologically rich languages. We thus demonstrate that byte-level pretrained language models can achieve excellent performance for morphologically rich languages, outperforming tokenizer-based models and presenting an important vector of exploration when constructing NLP pipelines for such languages.
MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models
Authors: Sarfaroz Yunusov, Hamza Sidat, Ali Emami
Date: 2024-09-20 00:00:00
Tasks: Diversity

This study explores the effectiveness of Large Language Models (LLMs) in creating personalized "mirror stories" that reflect and resonate with individual readers' identities, addressing the significant lack of diversity in literature. We present MirrorStories, a corpus of 1,500 personalized short stories generated by integrating elements such as name, gender, age, ethnicity, reader interest, and story moral. We demonstrate that LLMs can effectively incorporate diverse identity elements into narratives, with human evaluators identifying personalized elements in the stories with high accuracy. Through a comprehensive evaluation involving 26 diverse human judges, we compare the effectiveness of MirrorStories against generic narratives. We find that personalized LLM-generated stories not only outscore generic human-written and LLM-generated ones across all metrics of engagement (with average ratings of 4.22 versus 3.37 on a 5-point scale), but also achieve higher textual diversity while preserving the intended moral. We also provide analyses that include bias assessments and a study on the potential for integrating images into personalized stories.
Aligning Language Models Using Follow-up Likelihood as Reward Signal
Authors: Chen Zhang, Dading Chong, Feng Jiang, Chengguang Tang, Anningzhe Gao, Guohua Tang, Haizhou Li
Date: 2024-09-20 00:00:00
Tasks: Language Modelling

In natural human-to-human conversations, participants often receive feedback signals from one another based on their follow-up reactions. These reactions can include verbal responses, facial expressions, changes in emotional state, and other non-verbal cues. Similarly, in human-machine interactions, the machine can leverage the user's follow-up utterances as feedback signals to assess whether it has appropriately addressed the user's request. Therefore, we propose using the likelihood of follow-up utterances as rewards to differentiate preferred responses from less favored ones, without relying on human or commercial LLM-based preference annotations. Our proposed reward mechanism, ``Follow-up Likelihood as Reward" (FLR), matches the performance of strong reward models trained on large-scale human or GPT-4 annotated data on 8 pairwise-preference and 4 rating-based benchmarks. Building upon the FLR mechanism, we propose to automatically mine preference data from the online generations of a base policy model. The preference data are subsequently used to boost the helpfulness of the base model through direct alignment from preference (DAP) methods, such as direct preference optimization (DPO). Lastly, we demonstrate that fine-tuning the language model that provides follow-up likelihood with natural language feedback significantly enhances FLR's performance on reward modeling benchmarks and effectiveness in aligning the base policy model's helpfulness.
Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM
Authors: Zheng Wei Lim, Nitish Gupta, Honglin Yu, Trevor Cohn
Date: 2024-09-20 00:00:00
Tasks: Translation

Multilingual large language models (LLMs) are great translators, but this is largely limited to high-resource languages. For many LLMs, translating in and out of low-resource languages remains a challenging task. To maximize data efficiency in this low-resource setting, we introduce Mufu, which includes a selection of automatically generated multilingual candidates and an instruction to correct inaccurate translations in the prompt. Mufu prompts turn a translation task into a postediting one, and seek to harness the LLM's reasoning capability with auxiliary translation candidates, from which the model is required to assess the input quality, align the semantics cross-lingually, copy from relevant inputs and override instances that are incorrect. Our experiments on En-XX translations over the Flores-200 dataset show LLMs finetuned against Mufu-style prompts are robust to poor quality auxiliary translation candidates, achieving performance superior to NLLB 1.3B distilled model in 64% of low- and very-low-resource language pairs. We then distill these models to reduce inference cost, while maintaining on average 3.1 chrF improvement over finetune-only baseline in low-resource translations.
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank
Authors: Jaewook Lee, Hunter McNichols, Andrew Lan
Date: 2024-09-21 00:00:00
Tasks: Diversity

In this paper, we study an under-explored area of language and vocabulary learning: keyword mnemonics, a technique for memorizing vocabulary through memorable associations with a target word via a verbal cue. Typically, creating verbal cues requires extensive human effort and is quite time-consuming, necessitating an automated method that is more scalable. We propose a novel overgenerate-and-rank method via prompting large language models (LLMs) to generate verbal cues and then ranking them according to psycholinguistic measures and takeaways from a pilot user study. To assess cue quality, we conduct both an automated evaluation of imageability and coherence, as well as a human evaluation involving English teachers and learners. Results show that LLM-generated mnemonics are comparable to human-generated ones in terms of imageability, coherence, and perceived usefulness, but there remains plenty of room for improvement due to the diversity in background and preference among language learners.