GenAI4ELab/papercli-papers huggingface dataset:
Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs
Authors: Manon Reusens; Bart Baesens; David Jurgens
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Personalized Large Language Models (LLMs) are increasingly used in diverse applications, where they are assigned a specific persona—such as a happy high school teacher—to guide their responses. While prior research has examined how well LLMs adhere to predefined personas in writing style, a comprehensive analysis of consistency across different personas and task types is lacking. In this paper, we introduce a new standardized framework to analyze consistency in persona-assigned LLMs. We define consistency as the extent to which a model maintains coherent responses when assigned the same persona across different tasks and runs. Our framework evaluates personas across four different categories (happiness, occupation, personality, and political stance) spanning multiple task dimensions (survey writing, essay generation, social media post generation, single turn, and multi-turn conversations). Our findings reveal that consistency is influenced by multiple factors, including the assigned persona, stereotypes, and model design choices. Consistency also varies across tasks, increasing with more structured tasks and additional context. All code is available on GitHub.
Can you SPLICE it together? A Human Curated Benchmark for Probing Visual Reasoning in VLMs
Authors: Mohamad Ballout; Okajevo Wilfred; Seyedalireza Yaghoubi; Nohayr Muhammad Abdelmoneim; Julius Mayer; Elia Bruni
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In this work, we introduce SPLICE, a human-curated benchmark derived from the COIN instructional video dataset, designed to probe event-based reasoning across multiple dimensions: temporal, causal, spatial, contextual, and general knowledge. SPLICE includes 3,381 human-filtered videos spanning 12 categories and 180 sub-categories, such as sports, engineering, and housework. These videos are segmented into a total of 11,423 event clips. We evaluate both human participants and state-of-the-art vision-language models (VLMs) on the task of rearranging these clips into coherent event sequences to assess visual reasoning capabilities. Results reveal a significant gap: VLMs struggle to match human performance. While human-annotated textual descriptions improve model accuracy, they do not affect human performance, suggesting that models rely more on language priors than on visual understanding. Even with annotations, VLMs fall short of human-level reasoning, underscoring persistent challenges in visual reasoning. A deeper analysis across sub-categories shows that VLMs perform relatively better on videos where temporal and causal reasoning are dominant, compared to those where contextual and spatial reasoning are dominant. They also perform better on everyday tasks than on specialized ones.
On the Effectiveness of Prompt-Moderated LLMs for Math Tutoring at the Tertiary Level
Authors: Sebastian Steindl; Fabian Brunner; Nada Sissouno; Dominik Schwagerl; Florian Schöler-Niewiera; Ulrich Schäfer
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) have been studied intensively in the context of education, yielding heterogeneous results. Nowadays, these models are also deployed in formal education institutes. While specialized models exist, using prompt-moderated LLMs is widespread. In this study, we therefore investigate the effectiveness of prompt-moderated LLMs for math tutoring at a tertiary-level. We conduct a three-phase study with students (N=49) first receiving a review of the topics, then solving exercises, and finally writing an exam. During the exercises, they are presented with different types of assistance. We analyze the effect of LLM usage on the students’ performance, their engagement with the LLM, and their conversation strategies. Our results show that the prompt-moderation had a negative influence when compared to an unmoderated LLM. However, when the assistance was removed again, both LLM groups performed better than the control group, contradicting concerns about shallow learning. We publish the annotated conversations as a dataset to foster future research.
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context
Authors: Hairu Wang; Yuan Feng; Yukun Cao; Xike Xie; S Kevin Zhou
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models excel at many tasks but often incur high inference costs during deployment. To mitigate hallucination, many systems use a knowledge graph to enhance retrieval-augmented generation (KG-RAG). However, the large amount of retrieved knowledge contexts increase these inference costs further. A promising solution to balance performance and cost is LLM routing, which directs simple queries to smaller LLMs and complex ones to larger LLMs. However, no dedicated routing methods currently exist for RAG, and existing training-based routers face challenges scaling to this domain due to the need for extensive training data. We observe that the score distributions produced by the retrieval scorer strongly correlate with query difficulty. Based on this, we propose an extremely simple yet effective routing framework, the first specifically designed for KG-RAG that efficiently balances performance and cost in a plug-and-play manner. It delivers over 3x higher routing effectiveness while reducing runtime to less than 0.001x compared to existing methods. Our code is available at https://github.com/hrwang00/SkewRoute.
Acquiescence Bias in Large Language Models
Authors: Daniel Braun
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Acquiescence bias, i.e. the tendency of humans to agree with statements in surveys, independent of their actual beliefs, is well researched and documented. Since Large Language Models (LLMs) have been shown to be very influenceable by relatively small changes in input and are trained on human-generated data, it is reasonable to assume that they could show a similar tendency. We present a study investigating the presence of acquiescence bias in LLMs across different models, tasks, and languages (English, German, and Polish). Our results indicate that, contrary to humans, LLMs display a bias towards answering no, regardless of whether it indicates agreement or disagreement.
Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
Authors: Niv Eckhaus; Uri Berger; Gabriel Stanovsky
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: LLMs are used predominantly in synchronous communication, where a human user and a model communicate in alternating turns. In contrast, many real-world settings are asynchronous. For example, in group chats, online team meetings, or social games, there is no inherent notion of turns. In this work, we develop an adaptive asynchronous LLM agent consisting of two modules: a generator that decides what to say, and a scheduler that decides when to say it. To evaluate our agent, we collect a unique dataset of online Mafia games, where our agent plays with human participants. Overall, our agent performs on par with human players, both in game performance metrics and in its ability to blend in with the other human players. Our analysis shows that the agent’s behavior in deciding when to speak closely mirrors human patterns, although differences emerge in message content. We make all of our code and data publicly available. This work paves the way for integration of LLMs into realistic human group settings, from assistance in team discussions to educational and professional environments where complex social dynamics must be navigated.
How Sampling Affects the Detectability of Machine-written texts: A Comprehensive Study
Authors: Matthieu Dubois; François Yvon; Pablo Piantanida
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: As texts generated by Large Language Models (LLMs) are ever more common and often indistinguishable from human-written content, research on automatic text detection has attracted growing attention. Many recent detectors report near-perfect accuracy, often boasting AUROC scores above 99%. However, these claims typically assume fixed generation settings, leaving open the question of how robust such systems are to changes in decoding strategies. In this work, we systematically examine how sampling-based decoding impacts detectability, with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance. We find that even minor adjustments to decoding parameters - such as temperature, top-p, or nucleus sampling - can severely impair detector accuracy, with AUROC dropping from near-perfect levels to 1% in some settings. Our findings expose critical blind spots in current detection methods and emphasize the need for more comprehensive evaluation protocols. To facilitate future research, we release a large-scale dataset encompassing 37 decoding configurations, along with our code and evaluation framework https://github.com/BaggerOfWords/Sampling-and-Detection.
An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems
Authors: Sebastian Steindl; André Kestler; Ulrich Schäfer; Bernd Ludwig
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) have recently been studied within the context of Task-Oriented Dialogues (TOD). However, previous research is inconclusive on their effectiveness, with some studies claiming that LLMs are unable to perform the TOD task and others making sophisticated additions to their setup and coming to opposite conclusions. In this work, we take a detailed look at previous results that state LLMs perform insufficiently as a TOD system. As a result, we propose an updated, stronger baseline for multiple out-of-the-box LLM performances as TOD systems. We introduce a Self-Checking mechanism as a simple, yet effective, component to drastically improve their performance. Our results show that newer, pre-trained LLMs can, in fact, perform as TOD systems out-of-the-box, challenging the previous understanding. We show that LLMs can even perform competitively to fine-tuned models in certain metrics. Based on this, we propose directions for future research. Our code is published on Github.
MATCH: Task-Driven Code Evaluation through Contrastive Learning
Authors: Marah Ghoummaid; Vladimir Tchuiev; Ofek Glick; Michal Moshkovitz; Dotan Di Castro
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: AI-based code generation is increasingly prevalent, with GitHub Copilot estimated to generate 46% of the code on GitHub. Accurately evaluating how well generated code aligns with developer intent remains a critical challenge. Traditional evaluation methods, such as unit tests, are often unscalable and costly. Syntactic similarity metrics (e.g., BLEU, ROUGE) fail to capture code functionality, and metrics like CodeBERTScore require reference code, which is not always available. To address the gap in reference-free evaluation, with few alternatives such as ICE-Score, this paper introduces MATCH, a novel reference-free metric. MATCH uses Contrastive Learning to generate meaningful embeddings for code and natural language task descriptions, enabling similarity scoring that reflects how well generated code implements the task. We show that MATCH achieves stronger correlations with functional correctness and human preference than existing metrics across multiple programming languages.
Evaluating Large Language Models for Cross-Lingual Retrieval
Authors: Longfei Zuo; Pingjun Hong; Oliver Kraus; Barbara Plank; Robert Litschko
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Multi-stage information retrieval (IR) has become a widely-adopted paradigm in search. While Large Language Models (LLMs) have been extensively evaluated as second-stage reranking models for monolingual IR, a systematic large-scale comparison is still lacking for cross-lingual IR (CLIR). Moreover, while prior work shows that LLM-based rerankers improve CLIR performance, their evaluation setup relies on machine translation (MT) for the first stage. This is not only prohibitively expensive but also prone to error propagation across stages. Our evaluation on passage-level and document-level CLIR reveals that this setup, which we term noisy monolingual IR, is favorable for LLMs. However, LLMs still fail to improve the first-stage ranking if instead produced by multilingual bi-encoders. We further show that pairwise rerankers based on instruction-tuned LLMs perform competitively with listwise rerankers. To the best of our knowledge, we are the first to study the interaction between retrievers and rerankers in two-stage CLIR with LLMs. Our findings reveal that, without MT, current state-of-the-art rerankers fall severely short when directly applied in CLIR.
SGCD: Subtask-Guided Causal-Debiasing Framework for Robust Cross-Utterance Sentiment Quadruple Extraction in Dialogues
Authors: Xiang Li; Keyu Yao; Gang Shen
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The rise of digital social media has generated a vast amount of conversational data on platforms like Twitter and Reddit, allowing users to express sentiments through multi-turn dialogues. Dialogue-level aspect-based sentiment quadruple analysis (DiaASQ) seeks to extract structured information in the form of quadruples from these dialogues. However, it encounters challenges related to cross-utterance elements and focus bias. To address these issues, we introduce the Subtask-Guided and Causal-Debiasing (SGCD) framework. This framework leverages subtask-specific features to guide the learning of token-level features, which are then adaptively combined at the utterance level to meet specific semantic requirements. The SGCD framework employs multi-granularity attention paths to enhance cross-utterance matching and dialogue structure modeling. It also incorporates structural causal graphs and inverse probability weighting to mitigate biases from speakers and thread structures. Experimental results demonstrate that SGCD outperforms state-of-the-art methods, improving semantic modeling and bias robustness. This approach provides an effective solution for structured sentiment analysis in complex dialogues.
FaMTEB: Massive Text Embedding Benchmark in Persian Language
Authors: Erfan Zinvandi; Morteza Alikhani; Mehran Sarmadi; Zahra Pourbahman; Sepehr Arvin; Reza Kazemi; Arash Amini
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In this paper, we introduce a comprehensive benchmark for Persian (Farsi) text embeddings, built upon the Massive Text Embedding Benchmark (MTEB). Our benchmark includes 63 datasets spanning seven different tasks: classification, clustering, pair classification, reranking, retrieval, summary retrieval, and semantic textual similarity. The datasets are a combination of existing, translated, and newly generated (synthetic) data, offering a diverse and robust evaluation framework for Persian language models. All newly translated and synthetic datasets were rigorously evaluated by both humans and automated systems to ensure high quality and reliability. Given the growing adoption of text embedding models in chatbots, evaluation datasets are becoming an essential component of chatbot development and Retrieval-Augmented Generation (RAG) systems. As a contribution, we include chatbot evaluation datasets in the MTEB benchmark for the first time. Additionally, we introduce the novel task of summary retrieval, which is not included in the standard MTEB tasks. Another key contribution of this work is the introduction of a substantial number of new Persian-language NLP datasets for both training and evaluation, many of which have no existing counterparts in Persian. We evaluate the performance of several Persian and multilingual embedding models across a wide range of tasks. This work presents an open-source benchmark with datasets, accompanying code, and a public leaderboard.
Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training
Authors: Kazuma Kobayashi; Zhen Wan; Fei Cheng; Yuma Tsuta; Xin Zhao; Junfeng Jiang; Jiahao Huang; Zhiyi Huang; Yusuke Oda; Rio Yokota; Yuki Arase; Daisuke Kawahara; Akiko Aizawa; Sadao Kurohashi
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Limited low-resource language corpora in professional domains like medicine hinder cross-lingual domain adaptation of pre-trained large language models (PLMs). While abundant English medical corpora could complement this scarcity, the effective mixture of English and target language, including machine-translated content, remains underexplored. We examined how linguistic features (e.g., token sizes and language proportions) affect performance on a Japanese–English medical knowledge benchmark. Through continued pre-training of a bilingual PLM on multilingual corpora with varying proportions of English and Japanese texts (both original and machine-translated), we analyzed correlations between linguistic features and fine-grained task performance. Our findings suggest a practical approach to optimizing multilingual corpora for cross-lingual domain adaptation, which requires leveraging specialized knowledge from English corpora while ensuring sufficient coverage of language-specific expressions in a target language (Japanese). Such insights will contribute to the development of multilingual models that effectively leverage English-language resources in various professional domains with low-resource languages.
Structure Trumps Size: Rethinking Data Quality for LLM Reasoning
Authors: Hu Xu; Zeyan Li; Rui Wang; Jianfeng Xu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: As domain-specific datasets continue to expand, Large Language Models (LLMs) have achieved significant improvements across various fields through supervised fine-tuning (SFT). However, is more data always better for model fine-tuning? Through a series of controlled experiments, we discover that dataset structure—rather than mere size—plays a decisive role in enhancing LLM reasoning capabilities. While existing methods acknowledge that good data quality can make training more efficient, they primarily rely on simple heuristic strategies and lack systematic, quantitative frameworks for evaluating data quality. To address this gap, we introduce MCSQ—the first multi-dimensional quantitative framework for reasoning data management. MCSQ rigorously evaluates and optimizes datasets along six orthogonal dimensions. Through comprehensive controlled experiments, we find that selectively incorporating “distorted” (model-disagreed) or “mismatched” (low-relevance) samples—which are typically discarded in traditional approaches—can outperform conventional “clean” data on certain advanced reasoning benchmarks. Our findings challenge traditional assumptions about data “quality” in LLM fine-tuning and provide actionable, quantitative guidance for efficient, structure-aware dataset management. The datasets and codes are both available at https://github.com/xuhu0115/MCSQ.
A Zero-Shot Neuro-Symbolic Approach for Complex Knowledge Graph Question Answering
Authors: Prerna Agarwal; Srikanta Bedathur
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Existing low-resource Knowledge Graph Question Answering (KGQA) methods rely heavily on Large Language Models (LLMs) for semantic parsing of natural language question to its corresponding logical form (LF) such as SPARQL, S-Expression, etc. However, LLMs becomes bottleneck for practical applications due to: (i) its high computational resource requirements; (2) limited knowledge of LLM about different LFs; (3) unavailability of low-resource annotated data for new KGs and settings. This motivates us to design a KGQA framework that can operate in a zero-shot setting without the need for additional resources. In this paper, we propose (NS-KGQA): a zero-shot neuro-symbolic approach based on neural KG embeddings that have demonstrated their ability to effectively model KG structure without the need of additional data. We extract a link-prediction based symbolic question subgraph. We then propose a Symbolic Resolver that uses Dual KG Embeddings combined with a symbolic approach to resolve the symbolic question subgraph. Our extensive experiments on Complex KGQA benchmarks such as KQA Pro demonstrate the effectiveness of our approach. NS-KGQA outperforms all other LLM-based zero-shot baselines by 26% (avg).
Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization
Authors: Shuyang Hao; Yiwei Wang; Bryan Hooi; Jun Liu; Muhao Chen; Zi Huang; Yujun Cai
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In the realm of large vision-language models (LVLMs), adversarial jailbreak attacks serve as a red-teaming approach to identify safety vulnerabilities of these models and their associated defense mechanisms. However, we identify a critical limitation: not every adversarial optimization step leads to a positive outcome, and indiscriminately accepting optimization results at each step may reduce the overall attack success rate. To address this challenge, we introduce HKVE (Hierarchical Key-Value Equalization), an innovative jailbreaking framework that selectively accepts gradient optimization results based on the distribution of attention scores across different layers, ensuring that every optimization step positively contributes to the attack. Extensive experiments demonstrate HKVE’s significant effectiveness, achieving attack success rates of 75.08% on MiniGPT4, 85.84% on LLaVA and 81.00% on Qwen-VL, substantially outperforming existing methods by margins of 20.43%, 21.01% and 26.43% respectively. Furthermore, making every step effective not only leads to an increase in attack success rate but also allows for a reduction in the number of iterations, thereby lowering computational costs.