pwc-archive/papers-with-abstracts huggingface dataset:
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
Authors: Yingjian Chen, Haoran Liu, Yinhong Liu, Rui Yang, Han Yuan, Yanran Fu, Pengyuan Zhou, Qingyu Chen, James Caverlee, Irene Li
Date: 2025-02-23 00:00:00
Tasks: Fact Checking, Knowledge Graphs

Large language models (LLMs) are widely used, but they often generate subtle factual errors, especially in long-form text. These errors are fatal in some specialized domains such as medicine. Existing fact-checking with grounding documents methods face two main challenges: (1) they struggle to understand complex multihop relations in long documents, often overlooking subtle factual errors; (2) most specialized methods rely on pairwise comparisons, requiring multiple model calls, leading to high resource and computational costs. To address these challenges, we propose \textbf{\textit{GraphCheck}}, a fact-checking framework that uses extracted knowledge graphs to enhance text representation. Graph Neural Networks further process these graphs as a soft prompt, enabling LLMs to incorporate structured knowledge more effectively. Enhanced with graph-based reasoning, GraphCheck captures multihop reasoning chains which are often overlooked by existing methods, enabling precise and efficient fact-checking in a single inference call. Experimental results on seven benchmarks spanning both general and medical domains demonstrate a 6.1\% overall improvement over baseline models. Notably, GraphCheck outperforms existing specialized fact-checkers and achieves comparable performance with state-of-the-art LLMs, such as DeepSeek-V3 and OpenAI-o1, with significantly fewer parameters.
Reasoning About Persuasion: Can LLMs Enable Explainable Propaganda Detection?
Authors: Maram Hasanain, Md Arid Hasan, Mohamed Bayan Kmainasi, Elisa Sartori, Ali Ezzat Shahroor, Giovanni Da San Martino, Firoj Alam
Date: 2025-02-23 00:00:00
Tasks: Explanation Generation, Propaganda detection

There has been significant research on propagandistic content detection across different modalities and languages. However, most studies have primarily focused on detection, with little attention given to explanations justifying the predicted label. This is largely due to the lack of resources that provide explanations alongside annotated labels. To address this issue, we propose a multilingual (i.e., Arabic and English) explanation-enhanced dataset, the first of its kind. Additionally, we introduce an explanation-enhanced LLM for both label detection and rationale-based explanation generation. Our findings indicate that the model performs comparably while also generating explanations. We will make the dataset and experimental resources publicly available for the research community.
Revealing the Pragmatic Dilemma for Moral Reasoning Acquisition in Language Models
Authors: Guangliang Liu, Lei Jiang, Xitong Zhang, Kristen Marie Johnson
Date: 2025-02-23 00:00:00
Tasks: Ethics

Ensuring that Large Language Models (LLMs) return just responses which adhere to societal values is crucial for their broader application. Prior research has shown that LLMs often fail to perform satisfactorily on tasks requiring moral cognizance, such as ethics-based judgments. While current approaches have focused on fine-tuning LLMs with curated datasets to improve their capabilities on such tasks, choosing the optimal learning paradigm to enhance the ethical responses of LLMs remains an open research debate. In this work, we aim to address this fundamental question: can current learning paradigms enable LLMs to acquire sufficient moral reasoning capabilities? Drawing from distributional semantics theory and the pragmatic nature of moral discourse, our analysis indicates that performance improvements follow a mechanism similar to that of semantic-level tasks, and therefore remain affected by the pragmatic nature of morals latent in discourse, a phenomenon we name the pragmatic dilemma. We conclude that this pragmatic dilemma imposes significant limitations on the generalization ability of current learning paradigms, making it the primary bottleneck for moral reasoning acquisition in LLMs.
CodeCriticBench: A Holistic Code Critique Benchmark for Large Language Models
Authors: Alexander Zhang, Marcus Dong, Jiaheng Liu, Wei zhang, Yejie Wang, Jian Yang, Ge Zhang, Tianyu Liu, Zhongyuan Peng, Yingshui Tan, Yuanxing Zhang, Zhexu Wang, Weixun Wang, Yancheng He, Ken Deng, Wangchunshu Zhou, Wenhao Huang, Zhaoxiang Zhang
Date: 2025-02-23 00:00:00
Tasks: Code Generation, HumanEval, mbpp

The critique capacity of Large Language Models (LLMs) is essential for reasoning abilities, which can provide necessary suggestions (e.g., detailed analysis and constructive feedback). Therefore, how to evaluate the critique capacity of LLMs has drawn great attention and several critique benchmarks have been proposed. However, existing critique benchmarks usually have the following limitations: (1). Focusing on diverse reasoning tasks in general domains and insufficient evaluation on code tasks (e.g., only covering code generation task), where the difficulty of queries is relatively easy (e.g., the code queries of CriticBench are from Humaneval and MBPP). (2). Lacking comprehensive evaluation from different dimensions. To address these limitations, we introduce a holistic code critique benchmark for LLMs called CodeCriticBench. Specifically, our CodeCriticBench includes two mainstream code tasks (i.e., code generation and code QA) with different difficulties. Besides, the evaluation protocols include basic critique evaluation and advanced critique evaluation for different characteristics, where fine-grained evaluation checklists are well-designed for advanced settings. Finally, we conduct extensive experimental results of existing LLMs, which show the effectiveness of CodeCriticBench.
Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive Queries
Authors: Yin Wu, Quanyu Long, Jing Li, Jianfei Yu, Wenya Wang
Date: 2025-02-23 00:00:00
Tasks: Benchmarking, Image Retrieval, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) is a popular approach for enhancing Large Language Models (LLMs) by addressing their limitations in verifying facts and answering knowledge-intensive questions. As the research in LLM extends their capability to handle input modality other than text, e.g. image, several multimodal RAG benchmarks are proposed. Nonetheless, they mainly use textual knowledge bases as the primary source of evidences for augmentation. There still lack benchmarks designed to evaluate images as augmentation in RAG systems and how they leverage visual knowledge. We propose Visual-RAG, a novel Question Answering benchmark that emphasizes visual knowledge intensive questions. Unlike prior works relying on text-based evidence, Visual-RAG necessitates text-to-image retrieval and integration of relevant clue images to extract visual knowledge as evidence. With Visual-RAG, we evaluate 5 open-sourced and 3 proprietary Multimodal LLMs (MLLMs), revealing that images can serve as good evidence in RAG; however, even the SoTA models struggle with effectively extracting and utilizing visual knowledge
WildLong: Synthesizing Realistic Long-Context Instruction Data at Scale
Authors: Jiaxi Li, Xingxing Zhang, Xun Wang, Xiaolong Huang, Li Dong, Liang Wang, Si-Qing Chen, Wei Lu, Furu Wei
Date: 2025-02-23 00:00:00

Large language models (LLMs) with extended context windows enable tasks requiring extensive information integration but are limited by the scarcity of high-quality, diverse datasets for long-context instruction tuning. Existing data synthesis methods focus narrowly on objectives like fact retrieval and summarization, restricting their generalizability to complex, real-world tasks. WildLong extracts meta-information from real user queries, models co-occurrence relationships via graph-based methods, and employs adaptive generation to produce scalable data. It extends beyond single-document tasks to support multi-document reasoning, such as cross-document comparison and aggregation. Our models, finetuned on 150K instruction-response pairs synthesized using WildLong, surpasses existing open-source long-context-optimized models across benchmarks while maintaining strong performance on short-context tasks without incorporating supplementary short-context data. By generating a more diverse and realistic long-context instruction dataset, WildLong enhances LLMs' ability to generalize to complex, real-world reasoning over long contexts, establishing a new paradigm for long-context data synthesis.
Toward Responsible Federated Large Language Models: Leveraging a Safety Filter and Constitutional AI
Authors: Eunchung Noh, Jeonghun Baek
Date: 2025-02-23 00:00:00
Tasks: Federated Learning

Recent research has increasingly focused on training large language models (LLMs) using federated learning, known as FedLLM. However, responsible AI (RAI), which aims to ensure safe responses, remains underexplored in the context of FedLLM. In FedLLM, client data used for training may contain harmful content, leading to unsafe LLMs that generate harmful responses. Aggregating such unsafe LLMs into the global model and distributing them to clients may result in the widespread deployment of unsafe LLMs. To address this issue, we incorporate two well-known RAI methods into FedLLM: the safety filter and constitutional AI. Our experiments demonstrate that these methods significantly enhance the safety of the LLM, achieving over a 20% improvement on AdvBench, a benchmark for evaluating safety performance.
Uncovering the Hidden Threat of Text Watermarking from Users with Cross-Lingual Knowledge
Authors: Mansour Al Ghanim, Jiaqi Xue, Rochana Prih Hastuti, Mengxin Zheng, Yan Solihin, Qian Lou
Date: 2025-02-23 00:00:00

In this study, we delve into the hidden threats posed to text watermarking by users with cross-lingual knowledge. While most research focuses on watermarking methods for English, there is a significant gap in evaluating these methods in cross-lingual contexts. This oversight neglects critical adversary scenarios involving cross-lingual users, creating uncertainty regarding the effectiveness of cross-lingual watermarking. We assess four watermarking techniques across four linguistically rich languages, examining watermark resilience and text quality across various parameters and attacks. Our focus is on a realistic scenario featuring adversaries with cross-lingual expertise, evaluating the adequacy of current watermarking methods against such challenges.
Beyond Pattern Recognition: Probing Mental Representations of LMs
Authors: Moritz Miller, Kumar Shridhar
Date: 2025-02-23 00:00:00

Language Models (LMs) have demonstrated impressive capabilities in solving complex reasoning tasks, particularly when prompted to generate intermediate explanations. However, it remains an open question whether these intermediate reasoning traces represent a dynamic, evolving thought process or merely reflect sophisticated pattern recognition acquired during large scale pre training. Drawing inspiration from human cognition, where reasoning unfolds incrementally as new information is assimilated and internal models are continuously updated, we propose to delve deeper into the mental model of various LMs. We propose a new way to assess the mental modeling of LMs, where they are provided with problem details gradually, allowing each new piece of data to build upon and refine the model's internal representation of the task. We systematically compare this step by step mental modeling strategy with traditional full prompt methods across both text only and vision and text modalities. Experiments on the MathWorld dataset across different model sizes and problem complexities confirm that both text-based LLMs and multimodal LMs struggle to create mental representations, questioning how their internal cognitive processes work.
Entailment-Preserving First-order Logic Representations in Natural Language Entailment
Authors: Jinu Lee, Qi Liu, Runzhi Ma, Vincent Han, Ziqi Wang, Heng Ji, Julia Hockenmaier
Date: 2025-02-24 00:00:00
Tasks: Diversity, Learning-To-Rank

First-order logic (FOL) can represent the logical entailment semantics of natural language (NL) sentences, but determining natural language entailment using FOL remains a challenge. To address this, we propose the Entailment-Preserving FOL representations (EPF) task and introduce reference-free evaluation metrics for EPF, the Entailment-Preserving Rate (EPR) family. In EPF, one should generate FOL representations from multi-premise natural language entailment data (e.g. EntailmentBank) so that the automatic prover's result preserves the entailment labels. Experiments show that existing methods for NL-to-FOL translation struggle in EPF. To this extent, we propose a training method specialized for the task, iterative learning-to-rank, which directly optimizes the model's EPR score through a novel scoring function and a learning-to-rank objective. Our method achieves a 1.8-2.7% improvement in EPR and a 17.4-20.6% increase in EPR@16 compared to diverse baselines in three datasets. Further analyses reveal that iterative learning-to-rank effectively suppresses the arbitrariness of FOL representation by reducing the diversity of predicate signatures, and maintains strong performance across diverse inference types and out-of-domain data.
Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions
Authors: Joseph Suh, Erfan Jahanparast, Suhong Moon, Minwoo Kang, Serina Chang
Date: 2025-02-24 00:00:00
Tasks: Language Modeling, Language Modelling, Prompt Engineering, Survey

Large language models (LLMs) present novel opportunities in public opinion research by predicting survey responses in advance during the early stages of survey design. Prior methods steer LLMs via descriptions of subpopulations as LLMs' input prompt, yet such prompt engineering approaches have struggled to faithfully predict the distribution of survey responses from human subjects. In this work, we propose directly fine-tuning LLMs to predict response distributions by leveraging unique structural characteristics of survey data. To enable fine-tuning, we curate SubPOP, a significantly scaled dataset of 3,362 questions and 70K subpopulation-response pairs from well-established public opinion surveys. We show that fine-tuning on SubPOP greatly improves the match between LLM predictions and human responses across various subpopulations, reducing the LLM-human gap by up to 46% compared to baselines, and achieves strong generalization to unseen surveys and subpopulations. Our findings highlight the potential of survey-based fine-tuning to improve opinion prediction for diverse, real-world subpopulations and therefore enable more efficient survey designs. Our code is available at https://github.com/JosephJeesungSuh/subpop.
ATEB: Evaluating and Improving Advanced NLP Tasks for Text Embedding Models
Authors: Simeng Han, Frank Palma Gomez, Tu Vu, Zefei Li, Daniel Cer, Hansi Zeng, Chris Tar, Arman Cohan, Gustavo Hernandez Abrego
Date: 2025-02-24 00:00:00
Tasks: Information Retrieval, Instruction Following, Retrieval, Semantic Similarity, Semantic Textual Similarity

Traditional text embedding benchmarks primarily evaluate embedding models' capabilities to capture semantic similarity. However, more advanced NLP tasks require a deeper understanding of text, such as safety and factuality. These tasks demand an ability to comprehend and process complex information, often involving the handling of sensitive content, or the verification of factual statements against reliable sources. We introduce a new benchmark designed to assess and highlight the limitations of embedding models trained on existing information retrieval data mixtures on advanced capabilities, which include factuality, safety, instruction following, reasoning and document-level understanding. This benchmark includes a diverse set of tasks that simulate real-world scenarios where these capabilities are critical and leads to identification of the gaps of the currently advanced embedding models. Furthermore, we propose a novel method that reformulates these various tasks as retrieval tasks. By framing tasks like safety or factuality classification as retrieval problems, we leverage the strengths of retrieval models in capturing semantic relationships while also pushing them to develop a deeper understanding of context and content. Using this approach with single-task fine-tuning, we achieved performance gains of 8\% on factuality classification and 13\% on safety classification. Our code and data will be publicly available.
A Hybrid Approach to Information Retrieval and Answer Generation for Regulatory Texts
Authors: Jhon Rayo, Raul de la Rosa, Mario Garrido
Date: 2025-02-24 00:00:00
Tasks: Answer Generation, Information Retrieval, RAG, Retrieval, Retrieval-augmented Generation, Sentence

Regulatory texts are inherently long and complex, presenting significant challenges for information retrieval systems in supporting regulatory officers with compliance tasks. This paper introduces a hybrid information retrieval system that combines lexical and semantic search techniques to extract relevant information from large regulatory corpora. The system integrates a fine-tuned sentence transformer model with the traditional BM25 algorithm to achieve both semantic precision and lexical coverage. To generate accurate and comprehensive responses, retrieved passages are synthesized using Large Language Models (LLMs) within a Retrieval Augmented Generation (RAG) framework. Experimental results demonstrate that the hybrid system significantly outperforms standalone lexical and semantic approaches, with notable improvements in Recall@10 and MAP@10. By openly sharing our fine-tuned model and methodology, we aim to advance the development of robust natural language processing tools for compliance-driven applications in regulatory domains.
MultiOCR-QA: Dataset for Evaluating Robustness of LLMs in Question Answering on Multilingual OCR Texts
Authors: Bhawna Piryani, Jamshid Mozafari, Abdelrahman Abdallah, Antoine Doucet, Adam Jatowt
Date: 2025-02-24 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Question Answering

Optical Character Recognition (OCR) plays a crucial role in digitizing historical and multilingual documents, yet OCR errors -- imperfect extraction of the text, including character insertion, deletion and permutation -- can significantly impact downstream tasks like question-answering (QA). In this work, we introduce a multilingual QA dataset MultiOCR-QA, designed to analyze the effects of OCR noise on QA systems' performance. The MultiOCR-QA dataset comprises 60K question-answer pairs covering three languages, English, French, and German. The dataset is curated from OCR-ed old documents, allowing for the evaluation of OCR-induced challenges on question answering. We evaluate MultiOCR-QA on various levels and types of OCR errors to access the robustness of LLMs in handling real-world digitization errors. Our findings show that QA systems are highly prone to OCR induced errors and exhibit performance degradation on noisy OCR text.
Are Large Language Models Good Data Preprocessors?
Authors: Elyas Meguellati, Nardiena Pratama, Shazia Sadiq, Gianluca Demartini
Date: 2025-02-24 00:00:00
Tasks: Image Captioning

High-quality textual training data is essential for the success of multimodal data processing tasks, yet outputs from image captioning models like BLIP and GIT often contain errors and anomalies that are difficult to rectify using rule-based methods. While recent work addressing this issue has predominantly focused on using GPT models for data preprocessing on relatively simple public datasets, there is a need to explore a broader range of Large Language Models (LLMs) and tackle more challenging and diverse datasets. In this study, we investigate the use of multiple LLMs, including LLaMA 3.1 70B, GPT-4 Turbo, and Sonnet 3.5 v2, to refine and clean the textual outputs of BLIP and GIT. We assess the impact of LLM-assisted data cleaning by comparing downstream-task (SemEval 2024 Subtask "Multilabel Persuasion Detection in Memes") models trained on cleaned versus non-cleaned data. While our experimental results show improvements when using LLM-cleaned captions, statistical tests reveal that most of these improvements are not significant. This suggests that while LLMs have the potential to enhance data cleaning and repairing, their effectiveness may be limited depending on the context they are applied to, the complexity of the task, and the level of noise in the text. Our findings highlight the need for further research into the capabilities and limitations of LLMs in data preprocessing pipelines, especially when dealing with challenging datasets, contributing empirical evidence to the ongoing discussion about integrating LLMs into data preprocessing pipelines.
CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers
Authors: Anh Duc Le, Tu Vu, Nam Le Hai, Nguyen Thi Ngoc Diep, Linh Ngo Van, Trung Le, Thien Huu Nguyen
Date: 2025-02-24 00:00:00
Tasks: Knowledge Distillation

Large Language Models (LLMs) achieve state-of-the-art performance across various NLP tasks but face deployment challenges due to high computational costs and memory constraints. Knowledge distillation (KD) is a promising solution, transferring knowledge from large teacher models to smaller student models. However, existing KD methods often assume shared vocabularies and tokenizers, limiting their flexibility. While approaches like Universal Logit Distillation (ULD) and Dual-Space Knowledge Distillation (DSKD) address vocabulary mismatches, they overlook the critical \textbf{reasoning-aware distillation} aspect. To bridge this gap, we propose CoT2Align a universal KD framework that integrates Chain-of-Thought (CoT) augmentation and introduces Cross-CoT Alignment to enhance reasoning transfer. Additionally, we extend Optimal Transport beyond token-wise alignment to a sequence-level and layer-wise alignment approach that adapts to varying sequence lengths while preserving contextual integrity. Comprehensive experiments demonstrate that CoT2Align outperforms existing KD methods across different vocabulary settings, improving reasoning capabilities and robustness in domain-specific tasks.
Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization
Authors: Yao Xiao, Hai Ye, Linyao Chen, Hwee Tou Ng, Lidong Bing, XiaoLi Li, Roy Ka-Wei Lee
Date: 2025-02-24 00:00:00

Iterative data generation and model retraining are widely used to align large language models (LLMs). It typically involves a policy model to generate on-policy responses and a reward model to guide training data selection. Direct Preference Optimization (DPO) further enhances this process by constructing preference pairs of chosen and rejected responses. In this work, we aim to \emph{scale up} the number of on-policy samples via repeated random sampling to improve alignment performance. Conventional practice selects the sample with the highest reward as chosen and the lowest as rejected for DPO. However, our experiments reveal that this strategy leads to a \emph{decline} in performance as the sample size increases. To address this, we investigate preference data construction through the lens of underlying normal distribution of sample rewards. We categorize the reward space into seven representative points and systematically explore all 21 ($C_7^2$) pairwise combinations. Through evaluations on four models using AlpacaEval 2, we find that selecting the rejected response at reward position $\mu - 2\sigma$ rather than the minimum reward, is crucial for optimal performance. We finally introduce a scalable preference data construction strategy that consistently enhances model performance as the sample scale increases.