GenAI4ELab/papercli-papers huggingface dataset:
ETRQA: A Comprehensive Benchmark for Evaluating Event Temporal Reasoning Abilities of Large Language Models
Authors: Sigang Luo; Yinan Liu; Dongying Lin; Yingying Zhai; Bin Wang; Xiaochun Yang; Junpeng Liu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Event temporal reasoning (ETR) aims to model and reason about the relationships between events and time, as well as between events in the real world. Proficiency in ETR is a significant indicator that a large language model (LLM) truly understands the physical world. Previous question-answering datasets available for evaluating the ETR ability lack a systematic taxonomy and pay limited attention to compound questions. In this paper, we propose a unified taxonomy for event temporal questions and construct a comprehensive benchmark ETRQA, to evaluate the ETR abilities of LLMs based on this taxonomy. ETRQA not only inherits and expands the evaluation content of existing datasets but also contains multiple categories of compound questions. We evaluate two leading LLM series, Llama and Qwen, on ETRQA across various settings. Our experimental results indicate that large-scale LLMs exhibit certain ETR abilities. Yet they do not perform well in solving specific types of reasoning tasks, including reasoning involving time spans, reasoning for compound questions, and reasoning with fine temporal granularity. Additionally, we hope ETRQA can benefit the temporal reasoning research community for future studies.
The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing LLM Hallucination
Authors: Yuji Zhang; Sha Li; Cheng Qian; Jiateng Liu; Pengfei Yu; Chi Han; Yi R. Fung; Kathleen McKeown; ChengXiang Zhai; Manling Li; Heng Ji
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Hallucination is a persistent challenge in large language models (LLMs), where even with rigorous quality control, models often generate distorted facts. This paradox, in which error generation continues despite high-quality training data, calls for a deeper understanding of the underlying LLM mechanisms. To address it, we propose a novel concept: knowledge overshadowing, where model’s dominant knowledge can obscure less prominent knowledge during text generation, causing the model to fabricate inaccurate details. Building on this idea, we introduce a novel framework to quantify factual hallucinations by modeling knowledge overshadowing. Central to our approach is the log-linear law, which predicts that the rate of factual hallucination increases linearly with the logarithmic scale of (1) Knowledge Popularity, (2) Knowledge Length, and (3) Model Size. The law provides a means to preemptively quantify hallucinations, offering foresight into their occurrence even before model training or inference. Built on overshadowing effect, we propose a new decoding strategy CoDa, to mitigate hallucinations, which notably enhance model factuality on Overshadow (27.9%), MemoTrap (13.1%) and NQ-Swap (18.3%). Our findings not only deepen understandings of the underlying mechanisms behind hallucinations but also provide actionable insights for developing more predictable and controllable language models.
LegoMT2: Selective Asynchronous Sharded Data Parallel Training for Massive Neural Machine Translation
Authors: Fei Yuan; Yinquan Lu; Lei Li; Jingjing Xu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: It is a critical challenge to learn a single model for massive languages. Prior methods focus on increasing the model size and training data size. However, large models are difficult to optimize efficiently even with distributed parallel training and translation capacity can interfere among languages. To address the challenge, we propose LegoMT2, an efficient training approach with an asymmetric multi-way model architecture for massive multilingual neural machine translation. LegoMT2 shards 435 languages into 8 language-centric groups and attributes one local encoder for each group’s languages and a mix encoder-decoder for all languages. LegoMT2 trains the model through local data parallel and asynchronous distributed updating of parameters. LegoMT2 is 16.2× faster than the distributed training method for M2M-100-12B (which only for 100 languages) while improving the translation performance by an average of 2.2 BLEU on Flores-101, especially performing better for low-resource languages .
Pruning General Large Language Models into Customized Expert Models
Authors: Yiran Zhao; Guizhen Chen; Kenji Kawaguchi; Lidong Bing; Wenxuan Zhang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) have transformed natural language processing, yet their substantial model sizes often demand significant computational resources. To preserve computing resources and accelerate inference speed, it is crucial to prune redundant parameters, especially for experienced users who often need expert models tailored to specific downstream scenarios. However, current pruning methods primarily focus on maintaining models’ general capabilities, either requiring extensive post-training or performing poorly due to coarse-grained pruning. In this work, we design a  ̲Custom  ̲Pruning method (Cus-Prun) to prune a large general model into a smaller lightweight expert model, which is positioned along the “language”, “domain” and “task” dimensions. By identifying and pruning irrelevant neurons of each dimension, Cus-Prun creates expert models without any post-training. Our experiments demonstrate that Cus-Prun consistently outperforms other methods, achieving minimal loss in both expert and general capabilities across various models from different model families and sizes.
Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation
Authors: Xiaoxin Lu; Ranran Haoran Zhang; Yusen Zhang; Rui Zhang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: People get informed of a daily task plan through diverse media involving both texts and images. However, most prior research only focuses on LLM’s capability of textual plan generation. The potential of large-scale models in providing text-image plans remains understudied. Generating high-quality text-image plans faces two main challenges: ensuring consistent alignment between two modalities and keeping coherence among visual steps. To address these challenges, we propose a novel framework that generates and refines text-image plans step-by-step. At each iteration, our framework (1) drafts the next textual step based on the prediction history; (2) edits the last visual step to obtain the next one; (3) extracts PDDL-like visual information; and (4) refines the draft with the extracted visual information. The textual and visual step produced in stage (4) and (2) will then serve as inputs for the next iteration. Our approach offers a plug-and-play improvement to various backbone models, such as Mistral-7B, Gemini-1.5, and GPT-4o. To evaluate the effectiveness of our approach, we collect a new benchmark consisting of 1,100 tasks and their text-image pair solutions covering 11 daily topics. We also design and validate a new set of metrics to evaluate the multimodal consistency and coherence in text-image plans. Extensive experiment results show the effectiveness of our approach on a range of backbone models against competitive baselines.
Un-considering Contextual Information: Assessing LLMs’ Understanding of Indexical Elements
Authors: Metehan Oğuz; Yavuz Faruk Bakman; Duygu Nur Yaldiz
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) have demonstrated impressive performances in tasks related to coreference resolution. However, previous studies mostly assessed LLM performance on coreference resolution with nouns and third person pronouns. This study evaluates LLM performance on coreference resolution with indexical like I, you, here and tomorrow which come with unique challenges due to their linguistic properties. We present the first study examining how LLMs interpret indexicals in English, releasing the English Indexical Dataset with 1600 multiple-choice questions. We evaluate pioneering LLMs, including GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, and DeepSeek V3. Our results reveal that LLMs exhibit an impressive performance with some indexicals (I), while struggling with others (you, here, tomorrow), and that syntactic cues (e.g. quotation) contribute to LLM performance with some indexicals, while they reduce performance with others. Code and data are available at: https://github.com/metehanoguzz/LLMs-Indexicals-English
Behavioral Analysis of Information Salience in Large Language Models
Authors: Jan Trienes; Jörg Schlötterer; Junyi Jessy Li; Christin Seifert
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) excel at text summarization, a task that requires models to select content based on its importance. However, the exact notion of salience that LLMs have internalized remains unclear. To bridge this gap, we introduce an explainable framework to systematically derive and investigate information salience in LLMs through their summarization behavior. Using length-controlled summarization as a behavioral probe into the content selection process, and tracing the answerability of Questions Under Discussion throughout, we derive a proxy for how models prioritize information. Our experiments on 13 models across four datasets reveal that LLMs have a nuanced, hierarchical notion of salience, generally consistent across model families and sizes. While models show highly consistent behavior and hence salience patterns, this notion of salience cannot be accessed through introspection, and only weakly correlates with human perceptions of information salience.
The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs
Authors: Avinash Baidya; Kamalika Das; Xiang Gao
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Model (LLM)-based agents have significantly impacted Task-Oriented Dialog Systems (TODS) but continue to face notable performance challenges, especially in zero-shot scenarios. While prior work has noted this performance gap, the behavioral factors driving the performance gap remain under-explored. This study proposes a comprehensive evaluation framework to quantify the behavior gap between AI agents and human experts, focusing on discrepancies in dialog acts, tool usage, and knowledge utilization. Our findings reveal that this behavior gap is a critical factor negatively impacting the performance of LLM agents. Notably, as task complexity increases, the behavior gap widens (correlation: 0.963), leading to a degradation of agent performance on complex task-oriented dialogs. For the most complex task in our study, even the GPT-4o-based agent exhibits low alignment with human behavior, with low F1 scores for dialog acts (0.464), excessive and often misaligned tool usage with a F1 score of 0.139, and ineffective usage of external knowledge. Reducing such behavior gaps leads to significant performance improvement (24.3% on average). This study highlights the importance of comprehensive behavioral evaluations and improved alignment strategies to enhance the effectiveness of LLM-based TODS in handling complex tasks.
Task Facet Learning: A Structured Approach To Prompt Optimization
Authors: Gurusha Juneja; Gautam Jajoo; Hua Li; Jian Jiao; Nagarajan Natarajan; Amit Sharma
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Given a task in the form of a basic description and its training examples, prompt optimization is the problem of synthesizing the given information into a text prompt for a large language model. Humans solve this problem by also considering the different facets that define a task (e.g., counter-examples, explanations, analogies) and including them in the prompt. However, it is unclear whether existing algorithmic approaches, based on iteratively editing a given prompt or automatically selecting a few in-context examples, can cover the multiple facets required to solve a complex task. In this work, we view prompt optimization as that of learning multiple facets of a task from a set of training examples. We exploit structure in the prompt optimization problem and break down a prompt into loosely coupled semantic sections. The proposed algorithm, UniPrompt, (1) clusters the input space and uses clustered batches so that each batch likely corresponds to a different facet of the task, and (2) utilizes a feedback mechanism to propose adding, editing or deleting a section, which in turn is aggregated over a batch to capture generalizable facets. Empirical evaluation on multiple datasets and a real-world task shows that prompts generated using UniPrompt obtain higher accuracy than human-tuned prompts and those from state-of-the-art methods. In particular, our algorithm can generate long, complex prompts that existing methods are unable to generate.
LLM as Effective Streaming Processor: Bridging Streaming-Batch Mismatches with Group Position Encoding
Authors: Junlong Tong; Jinlan Fu; Zixuan Lin; Yingqi Fan; Anhao Zhao; Hui Su; Xiaoyu Shen
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) are primarily designed for batch processing. Existing methods for adapting LLMs to streaming rely either on expensive re-encoding or specialized architectures with limited scalability. This work identifies three key mismatches in adapting batch-oriented LLMs to streaming: (1) input-attention, (2) output-attention, and (3) position-ID mismatches. While it is commonly assumed that the latter two mismatches require frequent re-encoding, our analysis reveals that only the input-attention mismatch significantly impacts performance, indicating re-encoding outputs is largely unnecessary. To better understand this discrepancy with the common assumption,we provide the first comprehensive analysis of the impact of position encoding on LLMs in streaming, showing that preserving relative positions within source and target contexts is more critical than maintaining absolute order. Motivated by the above analysis, we introduce a group position encoding paradigm built on batch architectures to enhance consistency between streaming and batch modes. Extensive experiments on cross-lingual and cross-modal tasks demonstrate that our method outperforms existing approaches. Our method requires no architectural modifications, exhibits strong generalization in both streaming and batch modes. The code is available at repository https://github.com/EIT-NLP/StreamingLLM.
YinYang-Align: A new Benchmark for Competing Objectives and Introducing Multi-Objective Preference based Text-to-Image Alignment
Authors: Amitava Das; Yaswanth Narsupalli; Gurpreet Singh; Vinija Jain; Vasu Sharma; Suranjana Trivedy; Aman Chadha; Amit Sheth
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Precise alignment in Text-to-Image (T2I) systems is crucial for generating visuals that reflect user intent while adhering to ethical and policy standards. Recent controversies, such as the Google Gemini-generated Pope image backlash, highlight the urgent need for robust alignment mechanisms. Building on alignment successes in Large Language Models (LLMs), this paper introduces YinYangAlign, a benchmarking framework designed to evaluate and optimize T2I systems across six inherently contradictory objectives. These objectives highlight core trade-offs, such as balancing faithfulness to prompts with artistic freedom and maintaining cultural sensitivity without compromising creativity. Alongside this benchmark, we propose the Contradictory Alignment Optimization (CAO) framework, an extension of Direct Preference Optimization (DPO), which employs multi-objective optimization techniques to address these competing goals. By leveraging per-axiom loss functions, synergy-driven global preferences, and innovative tools like the Synergy Jacobian, CAO achieves superior alignment across all objectives. Experimental results demonstrate significant improvements in fidelity, diversity, and ethical adherence, setting new benchmarks for the field. This work provides a scalable, effective approach to resolving alignment challenges in T2I systems while offering insights into broader AI alignment paradigms.
FREE: Fast and Robust Vision Language Models with Early Exits
Authors: Divya Jyoti Bajpai; Manjesh Kumar Hanawal
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: In recent years, Vision-Language Models (VLMs) have shown remarkable performance improvements in Vision-Language tasks. However, their large size poses challenges for real-world applications where inference latency is a concern. To tackle this issue, we propose employing Early Exit (EE) strategies in VLMs. However, training exit classifiers in VLMs is challenging, particularly with limited labeled training data. To address this, we introduce FREE, an adversarial training approach within a GAN-based framework. Here, each exit consists of a transformer layer and a classifier. The transformer layer is adversarially trained to produce feature representations similar to the final layer, while a feature classifier serves as the discriminator. Our method focuses on performing input-adaptive inference that increases inference speed with minimal drop in performance. Experimental results demonstrate the effectiveness of our approach in enhancing accuracy and model robustness by mitigating overthinking and the phenomenon of mid-crisis that we highlight. We experimentally validate that our method speeds up the inference process by more than 1.51× while retaining comparable performance. The anonymized source code is available at https://github.com/Div290/BLIPEE.
REPRO-Bench: Can Agentic AI Systems Assess the Reproducibility of Social Science Research?
Authors: Chuxuan Hu; Liyun Zhang; Yeji Lim; Aum Wadhwani; Austin Peters; Daniel Kang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Assessing the reproducibility of social science papers is essential for promoting rigor in research processes, but manual assessment is costly. With recent advances in agentic AI systems (i.e., AI agents), we seek to evaluate their capability to automate this process. However, existing benchmarks for reproducing research papers (1) focus solely on reproducing results using provided code and data without assessing their consistency with the paper, (2) oversimplify real-world scenarios, and (3) lack necessary diversity in data formats and programming languages. To address these issues, we introduce REPRO-Bench, a collection of 112 task instances, each representing a social science paper with a publicly available reproduction report. The agents are tasked with assessing the reproducibility of the paper based on the original paper PDF and the corresponding reproduction package. REPRO-Bench features end-to-end evaluation tasks on the reproducibility of social science papers with complexity comparable to real-world assessments. We evaluate three representative AI agents on REPRO-Bench, with the best-performing agent achieving an accuracy of only 21.4%. Building on our empirical analysis, we develop REPRO-Agent, which improves the highest accuracy achieved by existing agents by 71%. We conclude that more advanced AI agents should be developed to automate real-world reproducibility assessment. REPRO-Bench is publicly available at https://github.com/uiuc-kang-lab/REPRO-Bench.
Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts
Authors: Sara Ghaboura; Ketan Pravin More; Ritesh Thawkar; Wafa Al Ghallabi; Omkar Thawakar; Fahad Shahbaz Khan; Hisham Cholakkal; Salman Khan; Rao Muhammad Anwer
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Understanding historical and cultural artifacts demands human expertise and advanced computational techniques, yet the process remains complex and time-intensive. While large multimodal models offer promising support, their evaluation and improvement require a standardized benchmark. To address this, we introduce TimeTravel, a benchmark of 10,250 expert-verified samples spanning 266 distinct cultures across 10 major historical regions. Designed for AI-driven analysis of manuscripts, artworks, inscriptions, and archaeological discoveries, TimeTravel provides a structured dataset and robust evaluation framework to assess AI models’ capabilities in classification, interpretation, and historical comprehension. By integrating AI with historical research, TimeTravel fosters AI-powered tools for historians, archaeologists, researchers, and cultural tourists to extract valuable insights while ensuring technology contributes meaningfully to historical discovery and cultural heritage preservation. We evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement. Our goal is to establish AI as a reliable partner in preserving cultural heritage, ensuring that technological advancements contribute meaningfully to historical discovery. We release the TimeTravel dataset and evaluation suite as open-source resources for culturally and historically informed research.
Unveiling and Addressing Pseudo Forgetting in Large Language Models
Authors: Huashan Sun; Yizhe Yang; Yinghao Li; Jiawei Li; Yang Gao
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Although substantial efforts have been made to mitigate catastrophic forgetting in continual learning, the intrinsic mechanisms are not well understood. In this work, we demonstrate the existence of “pseudo forgetting”: the performance degradation in previous tasks is not attributed to a loss of capabilities, but rather to the failure of the instructions to activate the appropriate model capabilities. We show that the model’s performance on previous tasks can be restored through two simple interventions: (1) providing partial external correct rationale, and (2) appending semantically meaningless suffixes to the original instructions, to guide the generation of correct rationales. Through empirical analysis of the internal mechanisms governing rationale generation, we reveal that models exhibiting pseudo forgetting show reduced instruction dependence during rationale generation, leading to suboptimal activation of their inherent capabilities. Based on this insight, we propose Rationale-Guidance Difficulty based Replay (RGD-R) framework that dynamically allocates replay data based on the model’s ability to correctly leverage the intrinsic capabilities. Experimental results demonstrate that RGD-R effectively mitigates pseudo forgetting while maintaining model plasticity.