GenAI4ELab/papercli-papers huggingface dataset:
Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs
Authors: William Sheffield; Kanishka Misra; Valentina Pyatkin; Ashwini Deo; Kyle Mahowald; Junyi Jessy Li
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Discourse particles are crucial elements that subtly shape the meaning of text. These words, often polyfunctional, give rise to nuanced and often quite disparate semantic/discourse effects,as exemplified by the diverse uses of the particle *just* (e.g., exclusive, temporal, emphatic). This work investigates the capacity of LLMs to distinguish the fine-grained senses of English *just*, a well-studied example in formal semantics, using data meticulously created and labeled by expert linguists. Our findings reveal that while LLMs exhibit some ability to differentiate between broader categories, they struggle to fully capture more subtle nuances, highlighting a gap in their understanding of discourse particles.
War of Thoughts: Competition Stimulates Stronger Reasoning in Large Language Models
Authors: Yibin Chen; Jinyi Liu; Yan Zheng; Yifu Yuan; Jianye Hao
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent advances in Large Language Models (LLMs) have reshaped the landscape of reasoning tasks, particularly through test-time scaling (TTS) to enhance LLM reasoning. Prior research has used structures such as trees or graphs to guide LLMs in searching for optimal solutions. These methods are time-consuming and require a strong reward model (RM) to support effective solution space exploration. Tournament-style approaches eliminate the reliance on RMs through comparative evaluation but suffer from transitivity dilemmas, leading to unstable ordering. To address these issues, we propose War of Thoughts (**WoT**), a novel post-hoc method that enhances reasoning without finetuning. WoT comprises two distinct stages: (1) *Exploration*, in which diverse and meaningful candidate solutions are generated through contrastive demonstrations and multi-granularity reasoning specifications; and (2) *Competition*, where these candidate solutions are subjected to multiple rounds of matchups within a competitive arena. Throughout this iterative process, the solutions are optimized and improved, with the optimal solution being determined based on Elo ratings. Extensive experiments across various LLMs demonstrate the superiority of WoT, surpassing baselines by **10–30%**. WoT can effectively stimulate stronger reasoning abilities, achieving impressive TTS performance in both generation budget and model size. It shows higher scalability efficiency compared to the baseline within the same budget. Notably, WoT exhibits excellent scalability with model size, even outperforming a 72B model despite using a 7B model.
Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation
Authors: Hoyun Song; Huije Lee; Jisu Shin; Sukmin Cho; Changgeon Ko; Jong C. Park
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The detection of mental health problems from social media and the interpretation of these results have been extensively explored. Research has shown that incorporating clinical symptom information into a model enhances domain expertise, improving its detection and interpretation performance. While large language models (LLMs) are shown to be effective for generating explanatory rationales in mental health detection, their substantially big parameter size and high computational cost limit their practicality. Reasoning distillation transfers this ability to smaller language models (SLMs), but inconsistencies in the relevance and domain alignment of LLM-generated rationales pose a challenge. This paper investigates how rationale quality impacts SLM performance in mental health detection and explanation generation. We hypothesize that ensuring high-quality and domain-relevant rationales enhances the distillation. To this end, we propose a framework that selects rationales based on their alignment with expert clinical reasoning. Experiments show that our quality-focused approach significantly enhances SLM performance in both mental disorder detection and rationale generation. This work highlights the importance of rationale quality and offers an insightful framework for knowledge transfer in mental health applications.
Rethinking Table Instruction Tuning
Authors: Naihao Deng; Rada Mihalcea
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent advances in table understanding have focused on instruction-tuning large language models (LLMs) for table-related tasks. However, existing research has overlooked the impact of hyperparameter choices, and also lacks a comprehensive evaluation of the out-of-domain table understanding ability and the general capabilities of these table LLMs. In this paper, we evaluate these abilities in existing table LLMs, and find significant declines in both out-of-domain table understanding and general capabilities as compared to their base models. Through systematic analysis, we show that hyperparameters, such as learning rate, can significantly influence both table-specific and general capabilities. Contrary to the previous table instruction-tuning work, we demonstrate that smaller learning rates and fewer training instances can enhance table understanding while preserving general capabilities. Based on our findings, we introduce TAMA, a TAble LLM instruction-tuned from LLaMA 3.1 8B Instruct, which achieves performance on par with, or surpassing GPT-3.5 and GPT-4 on table tasks, while maintaining strong out-of-domain generalization and general capabilities. Our findings highlight the potential for reduced data annotation costs and more efficient model development through careful hyperparameter selection. We open-source the project and our models.
CliniDial: A Naturally Occurring Multimodal Dialogue Dataset for Team Reflection in Action During Clinical Operation
Authors: Naihao Deng; Kapotaksha Das; Rada Mihalcea; Vitaliy Popov; Mohamed Abouelenien
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: In clinical operations, teamwork can be the crucial factor that determines the final outcome. Prior studies have shown that sufficient collaboration is the key factor that determines the outcome of an operation. To understand how the team practices teamwork during the operation, we collected **CliniDial** from simulations of medical operations. **CliniDial** includes the audio data and its transcriptions, the simulated physiology signals of the patient manikins, and how the team operates from two camera angles. We annotate behavior codes following an existing framework to understand the teamwork process for **CliniDial**. We pinpoint three main characteristics of our dataset, including its label imbalances, rich and natural interactions, and multiple modalities, and conduct experiments to test existing LLMs’ capabilities on handling data with these characteristics. Experimental results show that **CliniDial** poses significant challenges to the existing models, inviting future effort on developing methods that can deal with real-world clinical data. We open-source the codebase at https://github.com/MichiganNLP/CliniDial.
Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba)
Authors: Ruiqi He; Yushu He; Longju Bai; Jiarui Liu; Zhenjie Sun; Zenghao Tang; He Wang; Hanchen Xia; Rada Mihalcea; Naihao Deng
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Existing humor datasets and evaluations predominantly focus on English, leaving limited resources for culturally nuanced humor in non-English languages like Chinese. To address this gap, we construct **Chumor**, the first and the largest Chinese humor explanation dataset. **Chumor** is sourced from Ruo Zhi Ba (RZB, 弱智吧), a Chinese Reddit-like platform known for sharing intellectually challenging and culturally specific jokes. We test ten LLMs through direct and chain-of-thought prompting, revealing that **Chumor** poses significant challenges to existing LLMs, with their accuracy slightly above random and far below human. In addition, our analysis highlights that human-annotated humor explanations are significantly better than those generated by GPT-4o and ERNIE4-turbo. We release **Chumor** at https://huggingface.co/datasets/MichiganNLP/Chumor , our project page is at https://github.com/MichiganNLP/Chumor-2.0 , our leaderboard is at https://huggingface.co/spaces/MichiganNLP/Chumor-leaderboard , and our codebase is at https://github.com/MichiganNLP/Chumor-2.0 .
Explicit Bayesian Inference to Uncover the Latent Themes of Large Language Models
Authors: Raymond Li; Chuyuan Li; Gabriel Murray; Giuseppe Carenini
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large language models (LLMs) have demonstrated impressive generative capabilities, yet their inner mechanisms remain largely opaque. In this work, we introduce a novel approach to interpret LLMs generation process through the lens of an explicit Bayesian framework by inferring latent topic variables via variational inference. Specifically, we leverage a variational autoencoder-based neural topic model to dynamically approximate the posterior distribution over the high-level latent topic variables at each generation step. By reconstructing the LLM’s next-token predictions through these latent topics and maintaining a regularized latent space, our method yields interpretable and diverse topic representations but also has the ability to effectively captures semantic shifts throughout the text. We validate our approach on multiple datasets, showing that our latent topics outperform state-of-the-art topic models on intrinsic measures of coherence and diversity. Furthermore, we demonstrate the utility of our approach in downstream applications by using the inferred topic distributions to retrieve relevant demonstration examples for in-context learning, resulting in significant gains on classification and summarization tasks.
Improving Occupational ISCO Classification of Multilingual Swiss Job Postings with LLM-Refined Training Data
Authors: Ann-Sophie Gnehm; Simon Clematide
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Classifying occupations in multilingual job postings is challenging due to noisy labels, language variation, and domain-specific terminology. We present a method that refines silver-standard ISCO labels by consolidating them with predictions from pre-fine-tuned models, using large language model (LLM) evaluations to resolve discrepancies. The refined labels are used in Multiple Negatives Ranking (MNR) training for SentenceBERT-based classification. This approach substantially improves performance, raising Top-1 accuracy on silver data from 37.2% to 58.3% and reaching up to 80% precision on held-out data—an over 30-point gain validated by both GPT and human raters. The model benefits from cross-lingual transfer, with particularly strong gains in French and Italian. These results demonstrate hat LLM-guided label refinement can substantially improve multilingual occupation classification in fine-grained taxonomies such as CH-ISCO with 670 classes.
Brevity is the soul of sustainability: Characterizing LLM response lengths
Authors: Soham Poddar; Paramita Koley; Janardan Misra; Niloy Ganguly; Saptarshi Ghosh
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: A significant portion of the energy consumed by Large Language Models (LLMs) arises from their inference processes; hence developing energy-efficient methods for inference is crucial. While several techniques exist for inference optimization, output compression remains relatively unexplored, with only a few preliminary efforts addressing this aspect. In this work, we first benchmark 12 decoder-only LLMs across 5 datasets, revealing that these models often produce responses that are substantially longer than necessary. We then conduct a comprehensive quality assessment of LLM responses, formally defining six information categories present in LLM responses. We show that LLMs often tend to include redundant or additional information besides the minimal answer. To address this issue of long responses by LLMs, we explore several simple and intuitive prompt-engineering strategies.Empirical evaluation shows that appropriate prompts targeting length reduction and controlling information content can achieve significant energy optimization between 25-60% by reducing the response length while preserving the quality of LLM responses.
Adversarial Preference Learning for Robust LLM Alignment
Authors: Yuanfu Wang; Pengyu Wang; Chenyang Xi; Bo Tang; Junyi Zhu; Wenqiang Wei; Chen Chen; Chao Yang; Jingfeng Zhang; Chaochao Lu; Yijun Niu; Keming Mao; Zhiyu Li; Feiyu Xiong; Jie Hu; Mingchuan Yang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Modern language models often rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors. However, they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation, (2) the vast diversity of potential adversarial attacks, and (3) the risk of feedback bias and reward hacking. To address these challenges, we introduce Adversarial Preference Learning (APL), an iterative adversarial training method incorporating three key innovations. First, a direct harmfulness metric based on the model’s intrinsic preference probabilities, eliminating reliance on external assessment. Second, a conditional generative attacker that synthesizes input-specific adversarial variations. Third, an iterative framework with automated closed-loop feedback, enabling continuous adaptation through vulnerability discovery and mitigation. Experiments on Mistral-7B-Instruct-v0.3 demonstrate that APL significantly enhances robustness, achieving 83.33% harmlessness win rate over the base model (evaluated by GPT-4o), reducing harmful outputs from 5.88% to 0.43% (measured by LLaMA-Guard), and lowering attack success rate by up to 65% according to HarmBench. Notably, APL maintains competitive utility, with an MT-Bench score of 6.59 (comparable to the baseline 6.78) and an LC-WinRate of 46.52% against the base model.
gMBA: Expression Semantic Guided Mixed Boolean-Arithmetic Deobfuscation Using Transformer Architectures
Authors: Youjeong Roh; Joon-Young Paik; Jingun Kwon; Eun-Sun Cho
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Mixed Boolean-Arithmetic (MBA) obfuscation protects intellectual property by converting programs into forms that are more complex to analyze. However, MBA has been increasingly exploited by malware developers to evade detection and cause significant real-world problems. Traditional MBA deobfuscation methods often consider these expressions as part of a black box and overlook their internal semantic information. To bridge this gap, we propose a truth table, which is an automatically constructed semantic representation of an expression’s behavior that does not rely on external resources. The truth table is a mathematical form that represents the output of expression for all possible combinations of input. We also propose a general and extensible guided MBA deobfuscation framework (gMBA) that modifies a Transformer-based neural encoder-decoder Seq2Seq architecture to incorporate this semantic guidance. Experimental results and in-depth analysis show that integrating expression semantics significantly improves performance and highlights the importance of internal semantic expressions in recovering obfuscated code to its original form.
READoc: A Unified Benchmark for Realistic Document Structured Extraction
Authors: Zichao Li; Aizier Abulaiti; Yaojie Lu; Xuanang Chen; Jia Zheng; Hongyu Lin; Xianpei Han; Shanshan Jiang; Bin Dong; Le Sun
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Document Structured Extraction (DSE) aims to extract structured content from raw documents. Despite the emergence of numerous DSE systems, their unified evaluation remains inadequate, significantly hindering the field’s advancement. This problem is largely attributed to existing benchmark paradigms, which exhibit fragmented and localized characteristics. To offer a thorough evaluation of DSE systems, we introduce a novel benchmark named READoc, which defines DSE as a realistic task of converting unstructured PDFs into semantically rich Markdown. The READoc dataset is derived from 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo. In addition, we develop a DSE Evaluation S3uite comprising Standardization, Segmentation and Scoring modules, to conduct a unified evaluation of state-of-the-art DSE approaches. By evaluating a range of pipeline tools, expert visual models, and general Vision-Language Models, we identify the gap between current work and the unified, realistic DSE objective for the first time. We aspire that READoc will catalyze future research in DSE, fostering more comprehensive and practical solutions.
TicTac: Time-aware Supervised Fine-tuning for Automatic Text Dating
Authors: Han Ren; Minna Peng
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Pre-trained langauge models have achieved success in many natural language processing tasks, whereas they are trapped by the time-agnostic setting, impacting the performance in automatic text dating. This paper introduces TicTac, a supervised fine-tuning model for automatic text dating. Unlike the existing models that always ignore the temporal relatedness of documents, TicTac has the ability to learn temporal semantic information, which is helpful for capturing the temporal implications over long-time span corpora. As a fine-tuning framework, TicTac employs a contrastive learning-based approach to model two types of temporal relations of diachronic documents. TicTac also adopts a metric learning approach, where the temporal distance between a historical text and its category label is estimated, which benefits to learn temporal semantic information on texts with temporal ordering. Experiments on two diachronic corpora show that our model effectively captures the temporal semantic information and outperforms state-of-the-art baselines.
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting
Authors: Hao Feng; Shu Wei; Xiang Fei; Wei Shi; Yingdong Han; Lei Liao; Jinghui Lu; Binghong Wu; Qi Liu; Chunhui Lin; Jingqun Tang; Hao Liu; Can Huang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Current approaches either assemble specialized expert models or directly generate page-level content autoregressively, facing integration overhead, efficiency bottlenecks, and layout structure degradation despite their decent performance. To address these limitations, we present Dolphin ( Document Image Parsing via Heterogeneous Anchor Prompting), a novel multimodal document image parsing model following an analyze-then-parse paradigm. In the first stage, Dolphin generates a sequence of layout elements in reading order. These heterogeneous elements, serving as anchors and coupled with task-specific prompts, are fed back to Dolphin for parallel content parsing in the second stage. To train Dolphin, we construct a large-scale dataset of over 30 million samples, covering multi-granularity parsing tasks. Through comprehensive evaluations on both prevalent benchmarks and self-constructed ones, Dolphin achieves state-of-the-art performance across diverse page-level and element-level settings, while ensuring superior efficiency through its lightweight architecture and parallel parsing mechanism. The code and pre-trained models are publicly available at https://github.com/ByteDance/Dolphin
FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis
Authors: Yilun Zheng; Sha Li; Fangkun Wu; Yang Ziyi; Lin Hongchao; Zhichao Hu; Cai Xinjun; Ziming Wang; Jinxuan Chen; Sitao Luan; Jiahao Xu; Lihui Chen
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own, often for humor, provocation, or controversy. Detecting and analyzing parody can be challenging and is often reliant on context, yet it plays a crucial role in understanding cultural values, promoting subcultures, and enhancing self-expression. However, the study of parody is hindered by limited available data and deficient diversity in current datasets. To bridge this gap, we built seven parody datasets from both English and Chinese corpora, with 14,755 annotated users and 21,210 annotated comments in total. To provide sufficient context information, we also collect replies and construct user-interaction graphs to provide richer contextual information, which is lacking in existing datasets. With these datasets, we test traditional methods and Large Language Models (LLMs) on three key tasks: (1) parody detection, (2) comment sentiment analysis with parody, and (3) user sentiment analysis with parody. Our extensive experiments reveal that parody-related tasks still remain challenging for all models, and contextual information plays a critical role. Interestingly, we find that, in certain scenarios, traditional sentence embedding methods combined with simple classifiers can outperform advanced LLMs, i.e. DeepSeek-R1 and GPT-o3, highlighting parody as a significant challenge for LLMs.
P-CoT: A Pedagogically-motivated Participatory Chain-of-Thought Prompting for Phonological Reasoning in LLMs
Authors: Dongjun Jang; Youngchae Ahn; Hyopil Shin
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: This study explores the potential of phonological reasoning within text-based large language models (LLMs). Utilizing the PhonologyBench benchmark, we assess tasks like rhyme word generation, g2p conversion, and syllable counting. Our evaluations across 12 LLMs reveal that while few-shot learning offers inconsistent gains, the introduction of a novel Pedagogically-motivated Participatory Chain-of-Thought (P-CoT) prompt, which is anchored in educational theories like scaffolding and discovery learning, consistently enhances performance. This method leverages structured guidance to activate latent phonological abilities, achieving up to 52% improvement and even surpassing human baselines in certain tasks. Future work could aim to optimize P-CoT prompts for specific models or explore their application across different linguistic domains.