GenAI4ELab/papercli-papers huggingface dataset:
DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering
Authors: Rong Cheng; Jinyi Liu; Yan Zheng; Fei Ni; Jiazhen Du; Hangyu Mao; Fuzheng Zhang; Bo Wang; Jianye Hao
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Multi-Hop Question Answering (MHQA) tasks permeate real-world applications, posing challenges in orchestrating multi-step reasoning across diverse knowledge domains. While existing approaches have been improved with iterative retrieval, they still struggle to identify and organize dynamic knowledge. To address this, we propose DualRAG, a synergistic dual-process framework that seamlessly integrates reasoning and retrieval. DualRAG operates through two tightly coupled processes: Reasoning-augmented Querying (RaQ) and progressive Knowledge Aggregation (pKA). They work in concert: as RaQ navigates the reasoning path and generates targeted queries, pKA ensures that newly acquired knowledge is systematically integrated to support coherent reasoning. This creates a virtuous cycle of knowledge enrichment and reasoning refinement. Through targeted fine-tuning, DualRAG preserves its sophisticated reasoning and retrieval capabilities even in smaller-scale models, demonstrating its versatility and core advantages across different scales. Extensive experiments demonstrate that this dual-process approach substantially improves answer accuracy and coherence, approaching, and in some cases surpassing, the performance achieved with oracle knowledge access. These results establish DualRAG as a robust and efficient solution for complex multi-hop reasoning tasks.
Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World Model
Authors: Siheng Xiong; Ali Payani; Yuan Yang; Faramarz Fekri
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Enhancing the reasoning capabilities of language models (LMs) remains a key challenge, especially for tasks that require complex, multi-step decision-making where existing Chain-of-Thought (CoT) approaches struggle with consistency and verification. In this paper, we propose a novel reasoning framework, referred to as Structure-aware Planning with an Accurate World Model (SWAP), that integrates structured knowledge representation with learned planning. Unlike prior methods that rely purely on natural language reasoning, SWAP leverages entailment graphs to encode structured dependencies and enable symbolic verification of intermediate steps. To systematically construct and update the graph, SWAP employs a policy model to propose candidate expansions and a world model to predict structural updates. To improve accuracy, the world model generates multiple alternative updates, and a discriminator re-ranks them based on plausibility. To encourage diverse exploration, we introduce Diversity-based Modelling (DM), which samples candidates from the remaining probability mass after removing previously sampled candidates from the original policy distribution. Additionally, SWAP improves the discrimination accuracy through Contrastive Ranking (CR), which directly compares candidates within prompts and incorporates meta-knowledge to improve ranking quality. We evaluate SWAP across diverse reasoning-intensive benchmarks including math reasoning, logical reasoning, and coding tasks. Extensive experiments demonstrate that SWAP significantly improves upon the base models and consistently outperforms existing reasoning methods.
Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models
Authors: Xinxin Liu; Aaron Thomas; Cheng Zhang; Jianyi Cheng; Yiren Zhao; Xitong Gao
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Parameter-Efficient Fine-Tuning (PEFT) has gained prominence through low-rank adaptation methods like LoRA. In this paper, we focus on sparsity-based PEFT (SPEFT), which introduces trainable sparse adaptations to the weight matrices in the model, offering greater flexibility in selecting fine-tuned parameters compared to low-rank methods. We conduct the first systematic evaluation of salience metrics for SPEFT, inspired by zero-cost NAS proxies, and identify simple gradient-based metrics is reliable, and results are on par with the best alternatives, offering both computational efficiency and robust performance. Additionally, we compare static and dynamic masking strategies, finding that static masking, which predetermines non-zero entries before training, delivers efficiency without sacrificing performance, while dynamic masking offers no substantial benefits. Across NLP tasks, a simple gradient-based, static SPEFT consistently outperforms other fine-tuning methods for LLMs, providing a simple yet effective baseline for SPEFT. Our work challenges the notion that complexity is necessary for effective PEFT, while our open-source framework establishes a reproducible benchmark for future research.
Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse Attention
Authors: Emily Xiao; Chin-Jou Li; Yilin Zhang; Graham Neubig; Amanda Bertsch
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Many-shot in-context learning has recently shown promise as an alternative to finetuning, with the major advantage that the same model can be served for multiple tasks. However, this shifts the computational burden from training-time to inference-time, making deployment of many-shot ICL challenging to justify in-practice. This cost is further increased if a custom demonstration set is retrieved for each inference example. We present Dynamic Block-Sparse Attention, an optimized method for retrieval-based many-shot in-context learning. By combining carefully designed block-sparse attention and retrieval of cached groups of demonstrations, we achieve comparable per-example latency to finetuning while maintaining on average >95% of the best method’s accuracy across strong ICL and finetuning baselines. We hope that this will further enable the deployment of many-shot ICL at scale.
ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting
Authors: Rui Pan; Dylan Zhang; Hanning Zhang; Xingyuan Pan; Minrui Xu; Jipeng Zhang; Renjie Pi; Xiaoyu Wang; Tong Zhang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Bilevel optimization has shown its utility across various machine learning settings, yet most algorithms in practice require second-order information, making it challenging to scale them up. Only recently, a paradigm of first-order algorithms has emerged in the theoretical literature, capable of effectively addressing bilevel optimization problems. Nevertheless, the practical efficiency of this paradigm remains unverified, particularly in the context of large language models (LLMs). This paper introduces the first scalable instantiation of this paradigm called _ScaleBiO_, focusing on bilevel optimization for large-scale LLM data reweighting. By combining with a recently proposed memory-efficient training technique called LISA, our novel algorithm allows the paradigm to scale to ~30B-sized LLMs on 8×H100 GPUs, marking the first successful application of bilevel optimization under practical scenarios for large-sized LLMs. Empirically, extensive experiments on data reweighting verify the effectiveness of ScaleBiO for different-scaled models, including Llama-3-8B, Gemma-2-9B, Qwen-2-7B, and Qwen-2.5-32B, where bilevel optimization succeeds in instruction-following and math reasoning tasks, outperforming several popular baselines, including uniform sampling, influence-aware data filtering, and reference-model-based sampling methods. Theoretically, ScaleBiO ensures the optimality of the learned data weights, along with a convergence guarantee matching the conventional first-order bilevel optimization paradigm on smooth and strongly convex objectives.
PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference
Authors: Jiaming Ji; Donghai Hong; Borong Zhang; Boyuan Chen; Josef Dai; Boren Zheng; Tianyi Alex Qiu; Jiayi Zhou; Kaile Wang; Boxun Li; Sirui Han; Yike Guo; Yaodong Yang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: In this work, we introduce the PKU-SafeRLHF dataset, designed to promote research on safety alignment in large language models (LLMs). As a sibling project to SafeRLHF and BeaverTails, we separate annotations of helpfulness and harmlessness for question-answering pairs, providing distinct perspectives on these coupled attributes. Overall, we provide 44.6k refined prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels ranging from minor to severe, with answers generated by Llama-family models. Based on this, we collected 166.8k preference data, including dual-preference (helpfulness and harmlessness decoupled) and single-preference data (trade-off the helpfulness and harmlessness from scratch), respectively. Using the large-scale annotation data, we further train severity-sensitive moderation for the risk control of LLMs and safety-centric RLHF algorithms for the safety alignment of LLMs. We believe this dataset will be a valuable resource for the community, aiding in the safe deployment of LLMs.
What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective
Authors: Ming Li; Yanhong Li; Tianyi Zhou
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: What makes a difference in the post-training of LLMs? We investigate the training patterns of different layers in large language models (LLMs) through the lens of the gradient. We are specifically interested in how fast vs. slow thinking affects the layer-wise gradients, given the recent popularity of training LLMs on reasoning paths such as chain-of-thoughts (CoT) and process rewards. In our study, fast thinking without CoT leads to larger gradients and larger differences of gradients across layers than slow thinking (Detailed CoT), indicating the learning stability brought by the latter. Additionally, we study whether the gradient patterns can reflect the correctness of responses when training different LLMs using slow vs. fast thinking paths. The results show that the gradients of slow thinking can distinguish correct and irrelevant reasoning paths. As a comparison, we conduct similar gradient analyses on non-reasoning knowledge learning tasks, on which, however, trivially increasing the response length does not lead to similar behaviors of slow thinking. Our study strengthens fundamental understandings of LLM training and sheds novel insights on its efficiency and stability, which pave the way towards building a generalizable System-2 agent.
Beyond Text Compression: Evaluating Tokenizers Across Scales
Authors: Jonas F. Lotz; António V. Lopes; Stephan Peitz; Hendra Setiawan; Leonardo Emili
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The choice of tokenizer can profoundly impact language model performance, yet accessible and reliable evaluations of tokenizer quality remain an open challenge. Inspired by scaling consistency, we show that smaller models can accurately predict significant differences in tokenizer impact on larger models at a fraction of the compute cost. By systematically evaluating both English-centric and multilingual tokenizers, we find that tokenizer choice has negligible effects on tasks in English but results in consistent performance differences in multilingual settings. We propose new intrinsic tokenizer metrics inspired by Zipf’s law that correlate more strongly with downstream performance than text compression when modeling unseen languages. By combining several metrics to capture multiple aspects of tokenizer behavior, we develop a reliable framework for intrinsic tokenizer evaluations. Our work offers a more efficient path to informed tokenizer selection in future language model development.
Emergent Abilities of Large Language Models under Continued Pre-training for Language Adaptation
Authors: Ahmed Elhady; Eneko Agirre; Mikel Artetxe
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Continued pretraining (CPT) is a popular approach to adapt existing large language models (LLMs) to new languages. When doing so, it is common practice to include a portion of English data in the mixture, but its role has not been carefully studied to date. In this work, we show that including English does not impact validation perplexity, yet it is critical for the emergence of downstream capabilities in the target language. We introduce a language-agnostic benchmark for in-context learning (ICL), which reveals catastrophic forgetting early on CPT when English is not included. This in turn damages the ability of the model to generalize to downstream prompts as measured by perplexity, even if it does not manifest in terms of accuracy until later in training, and can be tied to a big shift in the model parameters. Based on these insights, we introduce curriculum learning and exponential moving average (EMA) of weights as effective alternatives to mitigate the need for English. All in all, our work sheds light into the dynamics by which emergent abilities arise when doing CPT for language adaptation, and can serve as a foundation to design more effective methods in the future.
R-Fairness: Assessing Fairness of Ranking in Subjective Data
Authors: Lorenzo Balzotti; Donatella Firmani; Jerin George Mathew; Riccardo Torlone; Sihem Amer-Yahia
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Subjective data, reflecting individual opinions, permeates platforms like Yelp and Amazon, influencing everyday decisions. Upon a user query, collaborative rating platforms return a collection of items ranked in an order that is often not transparent to the users. Then, each item is presented with a collection of reviews in an order that typically is, again, rather opaque. Despite the prevalence of such platforms, little attention has been given to fairness in their context, where groups writing best-ranked reviews for best-ranked items have more influence on users’ behavior. We design and evaluate a fairness assessment pipeline that starts with a data collection phase to gather reviews from real-world platforms, by submitting artificial user queries and iterating through rated items. Following that, a group assignment phase computes and infers relevant groups for each review, based on review content and user data. Finally, the third step assesses and evaluates the fairness of rankings for different user groups. The key contributions are comparing group exposure for different queries and platforms and comparing how popular fairness definitions behave in different settings. Experiments on real datasets reveal insights into the impact of item ranking on fairness computation and the varying robustness of these measures.
RePanda: Pandas-powered Tabular Verification and Reasoning
Authors: Atoosa Chegini; Keivan Rezaei; Hamid Eghbalzadeh; Soheil Feizi
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Fact-checking tabular data is essential for ensuring the accuracy of structured information in domains such as journalism, finance, and scientific research. However, existing methods often rely on black-box models with opaque reasoning. We introduce RePanda, a structured fact verification approach that translates claims into executable pandas queries, enabling interpretable and verifiable reasoning.To train RePanda, we construct PanTabFact, a structured dataset derived from TabFact, where claims are paired with executable queries generated using DeepSeek-Chat and refined through automated error correction. Fine-tuning DeepSeek-coder-7B-instruct-v1.5 on PanTabFact, RePanda achieves 84.09% accuracy on TabFact. To assess Out-of-Distribution (OOD) generalization, we create a dataset named WikiFact from WikiTableQuestions by transforming question-answer pairs into factual claims. Without additional fine-tuning, RePanda achieves 84.72% accuracy on WikiFact, significantly outperforming all other baselines and demonstrating strong OOD robustness. PanTabFact is publically available on HuggingFace at datasets/AtoosaChegini/PanTabFact.Beyond fact verification, RePanda extends to tabular question answering by generating executable queries that retrieve precise answers. To support this, we introduce PanWiki, a dataset mapping WikiTableQuestions to pandas queries. Fine-tuning on PanWiki, RePanda achieves 75.1% accuracy in direct answer retrieval. These results highlight the effectiveness of structured execution-based reasoning for tabular verification and question answering.
Towards Style Alignment in Cross-Cultural Translation
Authors: Shreya Havaldar; Adam Stein; Eric Wong; Lyle Ungar
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Successful communication depends on the speaker’s intended style (i.e., what the speaker is trying to convey) aligning with the listener’s interpreted style (i.e., what the listener perceives). However, cultural differences often lead to misalignment between the two; for example, politeness is often lost in translation. We characterize the ways that LLMs fail to translate style – biasing translations towards neutrality and performing worse in non-Western languages. We mitigate these failures with RASTA (Retrieval-Augmented STylistic Alignment), a method that leverages learned stylistic concepts to encourage LLM translation to appropriately convey cultural communication norms and align style.
TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining
Authors: Jeffrey Li; Mohammadreza Armandpour; Seyed Iman Mirzadeh; Sachin Mehta; Vaishaal Shankar; Raviteja Vemulapalli; Samy Bengio; Oncel Tuzel; Mehrdad Farajtabar; Hadi Pouransari; Fartash Faghri
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Large Language Models (LLMs) trained on historical web data inevitably become outdated. We investigate evaluation strategies and update methods for LLMs as new data becomes available. We introduce a web-scale dataset for time-continual pretraining of LLMs derived from 114 dumps of Common Crawl (CC) – orders of magnitude larger than previous continual language modeling benchmarks. We also design time-stratified evaluations across both general CC data and specific domains (Wikipedia, StackExchange, and code documentation) to assess how well various continual learning methods adapt to new data while retaining past knowledge. Our findings demonstrate that, on general CC data, autoregressive meta-schedules combined with a fixed-ratio replay of older data can achieve comparable held-out loss to re-training from scratch, while requiring significantly less computation (2.6x). However, the optimal balance between incorporating new data and replaying old data differs as replay is crucial to avoid forgetting on generic web data but less so on specific domains.
Entailed Between the Lines: Incorporating Implication into NLI
Authors: Shreya Havaldar; Hamidreza Alvari; John Palowitch; Mohammad Javad Hosseini; Senaka Buthpitiya; Alex Fabrikant
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Much of human communication depends on implication, conveying meaning beyond literal words to express a wider range of thoughts, intentions, and feelings. For models to better understand and facilitate human communication, they must be responsive to the text’s implicit meaning. We focus on Natural Language Inference (NLI), a core tool for many language tasks, and find that state-of-the-art NLI models and datasets struggle to recognize a range of cases where entailment is implied, rather than explicit from the text. We formalize implied entailment as an extension of the NLI task and introduce the Implied NLI dataset (INLI) to help today’s LLMs both recognize a broader variety of implied entailments and to distinguish between implicit and explicit entailment. We show how LLMs fine-tuned on INLI understand implied entailment and can generalize this understanding across datasets and domains.
Multi-Level Explanations for Generative Language Models
Authors: Lucas Monteiro Paes; Dennis Wei; Hyo Jin Do; Hendrik Strobelt; Ronny Luss; Amit Dhurandhar; Manish Nagireddy; Karthikeyan Natesan Ramamurthy; Prasanna Sattigeri; Werner Geyer; Soumya Ghosh
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Despite the increasing use of large language models (LLMs) for context-grounded tasks like summarization and question-answering, understanding what makes an LLM produce a certain response is challenging. We propose Multi-Level Explanations for Generative Language Models (MExGen), a technique to provide explanations for context-grounded text generation. MExGen assigns scores to parts of the context to quantify their influence on the model’s output. It extends attribution methods like LIME and SHAP to LLMs used in context-grounded tasks where (1) inference cost is high, (2) input text is long, and (3) the output is text. We conduct a systematic evaluation, both automated and human, of perturbation-based attribution methods for summarization and question answering. The results show that our framework can provide more faithful explanations of generated output than available alternatives, including LLM self-explanations. We open-source code for MExGen as part of the ICX360 toolkit: https://github.com/IBM/ICX360.
A Multi-Agent Framework for Mitigating Dialect Biases in Privacy Policy Question-Answering Systems
Authors: Đorđe Klisura; Astrid R Bernaga Torres; Anna Karen Gárate-Escamilla; Rajesh Roshan Biswal; Ke Yang; Hilal Pataci; Anthony Rios
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Privacy policies inform users about data collection and usage, yet their complexity limits accessibility for diverse populations. Existing Privacy Policy Question Answering (QA) systems exhibit performance disparities across English dialects, disadvantaging speakers of non-standard varieties. We propose a novel multi-agent framework inspired by human-centered design principles to mitigate dialectal biases. Our approach integrates a Dialect Agent, which translates queries into Standard American English (SAE) while preserving dialectal intent, and a Privacy Policy Agent, which refines predictions using domain expertise. Unlike prior approaches, our method does not require retraining or dialect-specific fine-tuning, making it broadly applicable across models and domains. Evaluated on PrivacyQA and PolicyQA, our framework improves GPT-4o-mini’s zero-shot accuracy from 0.394 to 0.601 on PrivacyQA and from 0.352 to 0.464 on PolicyQA, surpassing or matching few-shot baselines without additional training data. These results highlight the effectiveness of structured agent collaboration in mitigating dialect biases and underscore the importance of designing NLP systems that account for linguistic diversity to ensure equitable access to privacy information.