ACL 2025 论文列表

Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. Among these reasoning tasks, graph problems stand out due to their complexity and unique structural characteristics, attracting considerable attention from researchers. Previous studies have explored LLMs’ graph reasoning abilities through various techniques, such as different encoding methods for graph structures and the use of carefully designed prompts. However, a critical factor has been mostly overlooked: the prompt sequential order in which graph descriptions are presented to the models. In this study, we present the first comprehensive analysis of how the order of graph descriptions impacts LLM performance. Specifically, we comprehensively evaluate four graph description orders across six graph problems using six mainstream LLMs. The results reveal that: (1) ordered graph descriptions significantly improve LLMs’ comprehension of graph structures; (2) the robustness of LLMs to graph description order varies across different tasks; and (3) the impact of graph order on performance is closely related to the inherent characteristics of tasks. This study provides a critical advancement in the application of LLMs for solving graph-related problems, paving the way for future research to optimize model performance through strategic graph description ordering.

Pairwise preferences over model responses are widely collected to evaluate and provide feedback to large language models (LLMs). Given two alternative model responses to the same input, a human or AI annotator selects the “better” response. This approach can provide feedback for domains where other hard-coded metrics are difficult to obtain (e.g., chat response quality), thereby helping model evaluation or training. However, for some domains high-quality pairwise comparisons can be tricky to obtain - from AI and humans. For example, for responses with many factual statements, annotators may disproportionately weigh writing quality rather than underlying facts. In this work, we explore augmenting standard AI annotator systems with additional tools to improve performance on three challenging response domains: long-form factual, math and code tasks. We propose a tool-using agentic system to provide higher quality feedback on these domains. Our system uses web-search and code execution to ground itself based on external validation, independent of the LLM’s internal knowledge and biases. We provide extensive experimental results evaluating our method across the three targeted response domains as well as general annotation tasks, using RewardBench (incl. AlpacaEval and LLMBar), RewardMath, as well as three new datasets for domains with saturated pre-existing datasets. Our results indicate that external tools can indeed improve performance in many, but not all, cases. More generally, our experiments highlight the sensitivity of performance to simple parameters (e.g., prompt) and the need for improved (non-saturated) annotator benchmarks. We share our code at https://github.com/apple/ml-agent-evaluator.

Two commonly employed strategies to combat the rise of misinformation on social media are (i) fact-checking by professional organisations and (ii) community moderation by platform users. Policy changes by Twitter/X and, more recently, Meta, signal a shift away from partnerships with fact-checking organisations and towards an increased reliance on crowdsourced community notes. However, the extent and nature of dependencies between fact-checking and *helpful* community notes remain unclear. To address these questions, we use language models to annotate a large corpus of Twitter/X community notes with attributes such as topic, cited sources, and whether they refute claims tied to broader misinformation narratives. Our analysis reveals that community notes cite fact-checking sources up to five times more than previously reported. Fact-checking is especially crucial for notes on posts linked to broader narratives, which are *twice* as likely to reference fact-checking sources compared to other sources. Our results show that successful community moderation relies on professional fact-checking and highlight how citizen and professional fact-checking are deeply intertwined.

Instruction tuning is crucial for adapting large language models (LLMs) to align with user intentions. Numerous studies emphasize the significance of the quality of instruction tuning (IT) data, revealing a strong correlation between IT data quality and the alignment performance of LLMs. In these studies, the quality of IT data is typically assessed by evaluating the performance of LLMs trained with that data. However, we identified a prevalent issue in such practice: hyperparameters for training models are often selected arbitrarily without adequate justification. We observed significant variations in hyperparameters applied across different studies, even when training the same model with the same data. In this study, we demonstrate the potential problems arising from this practice and emphasize the need for careful consideration in verifying data quality. Through our experiments on the quality of LIMA data and a selected set of 1,000 Alpaca data points, we demonstrate that arbitrary hyperparameter decisions can make any arbitrary conclusion.

The use of large language models (LLMs) as automated evaluation tools to assess the quality of generated natural language, known as ”LLMs-as-Judges”, has demonstrated promising capabilities and is rapidly gaining widespread attention. However, when applied to pairwise comparisons of candidate responses, LLM-based evaluators often exhibit selection bias. Specifically, their judgments may become inconsistent when the option positions or ID tokens are swapped, compromising the effectiveness and fairness of the evaluation result. To address this challenge, we introduce CalibraEval, a novel label-free method for mitigating selection bias during inference. Specifically, CalibraEval reformulates debiasing as an optimization task aimed at adjusting observed prediction distributions to align with unbiased prediction distributions. To solve this optimization problem, we propose a non-parametric order-preserving algorithm (NOA). This algorithm leverages the partial order relationships between model prediction distributions, thereby eliminating the need for explicit labels and precise mathematical function modeling. Empirical evaluations of LLMs in multiple representative benchmarks demonstrate that CalibraEval effectively mitigates selection bias and improves performance compared to existing debiasing methods. This work marks a step toward building more robust and unbiased automated evaluation frameworks, paving the way for improved reliability in AI-driven assessments. The code can be found at https://github.com/CSHaitao/CalibraEval.

Despite rising global usage of large language models (LLMs), their ability to generate *long-form* answers to *culturally specific* questions remains unexplored in many languages. To fill this gap, we perform the first study of textual multilingual long-form QA by creating CaLMQA, a dataset of **51.7K** culturally specific questions across **23** different languages. We define culturally specific questions as those that refer to concepts unique to one or a few cultures, or have different answers depending on the cultural or regional context. We obtain these questions by crawling naturally-occurring questions from community web forums in high-resource languages, and by hiring native speakers to write questions in under-resourced, rarely-studied languages such as Fijian and Kirundi. Our data collection methodologies are translation-free, enabling the collection of culturally unique questions like “Kuber iki umwami wa mbere w’uburundi yitwa Ntare?” (Kirundi; English translation: “Why was the first king of Burundi called Ntare (Lion)?”). We evaluate factuality, relevance and surface-level quality of LLM-generated long-form answers, finding that (1) for many languages, even the best models make critical surface-level errors (e.g., answering in the wrong language, repetition), especially for low-resource languages; and (2) answers to culturally specific questions contain more factual errors than answers to culturally agnostic questions – questions that have consistent meaning and answer across many cultures. We release CaLMQA to facilitate future research in cultural and multilingual long-form QA.

NLP research has increasingly focused on subjective tasks such as emotion analysis. However, existing emotion benchmarks suffer fromtwo major shortcomings: (1) they largely rely on keyword-based emotion recognition, overlooking crucial cultural dimensions required fordeeper emotion understanding, and (2) many are created by translating English-annotated data into other languages, leading to potentially unreliable evaluation. To address these issues, we introduce Cultural Lenses on Emotion (CuLEmo), the first benchmark designedto evaluate culture-aware emotion prediction across six languages: Amharic, Arabic, English, German, Hindi, and Spanish. CuLEmocomprises 400 crafted questions per language, each requiring nuanced cultural reasoning and understanding. We use this benchmark to evaluate several state-of-the-art LLMs on culture-aware emotion prediction and sentiment analysis tasks. Our findings reveal that (1) emotion conceptualizations vary significantly across languages and cultures, (2) LLMs performance likewise varies by language and cultural context, and (3) prompting in English with explicit country context often outperforms in-language prompts for culture-aware emotion and sentiment understanding. The dataset and evaluation code is available.

Argument Mining (AM) involves the automatic identification of argument structure in natural language. Traditional AM methods rely on micro-structural features derived from the internal properties of individual Argumentative Discourse Units (ADUs). However, argument structure is shaped by a macro-structure capturing the functional interdependence among ADUs. This macro-structure consists of segments, where each segment contains ADUs that fulfill specific roles to maintain coherence within the segment (**local coherence**) and across segments (**global coherence**). This paper presents an approach that models macro-structure, capturing both local and global coherence to identify argument structures. Experiments on heterogeneous datasets demonstrate superior performance in both in-dataset and cross-dataset evaluations. The cross-dataset evaluation shows that macro-structure enhances transferability to unseen datasets.

Integrating multimodal clinical records—such as Electronic Health Records (EHR) and free-text clinical reports—has shown great potential in predicting clinical outcomes. However, prior work has primarily focused on capturing temporal interactions within individual samples and fusing multimodal information, overlooking critical temporal patterns across different patients. These patterns, such as trends in vital signs like abnormal heart rate or blood pressure, can indicate deteriorating health or an impending critical event of any individual in a given population. Similarly, clinical notes often contain textual descriptions that reflect these changes. Identifying corresponding temporal patterns across different modalities is crucial for improving the accuracy of clinical outcome predictions, yet it remains a challenging task. To address this gap, we introduce a Cross-modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data. Our approach introduces shared initial temporal pattern representations and refines them using slot attention to generate temporal semantic embeddings. To ensure rich cross-modal temporal semantics in the learned patterns, we introduce a Temporal Pattern Noise Contrastive Estimation (TP-NCE) loss for cross-modal alignment, along with two reconstruction losses to retain core information of each modality. Evaluations on two clinically critical tasks—48 hour in-hospital mortality and 24-hour phenotype classification—using the MIMIC-III database demonstrate the superiority of our method over existing approaches. The code is anonymously available at https://github.com/HKU-MedAI/CTPD.

Large Language Models (LLMs) have achieved remarkable success in natural language processing (NLP), particularly in single-turn question answering (QA) on short-text. However, their performance significantly declines when applied to multi-turn QA over extra-long context (ELC), as they struggle to capture the logical correlations across multiple chunks of ELC and maintain the coherence of multi-turn Questions. To address the challenges, we propose the CSTree-SRI framework (Cognitive Semantic Tree through Summarization, Retrieval, and Introspection). CSTree-SRI dynamically constructs the CSTree to preserve logical coherence within ELC through hierarchical synthesis and introspective validation. Then a logic-driven traversal strategy on CSTree is designed to provide efficient information retrieval for question answering. Additionally, we construct a suite of multi-turn QA datasets and an evaluation benchmark tailored for ELC tasks, and comprehensive experiments demonstrate the framework’s superiority in addressing the challenges of multi-turn QA over ELC.

A radiology report comprises several sections, including the Findings and Impression of the diagnosis. Automatically generating the Impression from the Findings is crucial for reducing radiologists’ workload and improving diagnostic accuracy. Pretrained models that excel in common abstractive summarization problems encounter challenges when applied to specialized medical domains largely due to the complex terminology and the necessity for accurate clinical context. Such tasks in medical domains demand extracting core information, avoiding context shifts, and maintaining proper flow. Misuse of medical terms can lead to drastic clinical errors. To address these issues, we introduce a sequential transfer learning that ensures key content extraction and coherent summarization. Sequential transfer learning often faces challenges like initial parameter decay and knowledge loss, which we resolve with the Fisher matrix regularization. Using MIMIC-CXR and Open-I datasets, our model, CSTRL — Context-driven Sequential TRansfer Learning — achieved state-of-the-art performance, showing 56.2% improvement in BLEU-1, 40.5% in BLEU-2, 84.3% in BLEU-3, 28.9% in ROUGE-1, 41.0% in ROUGE-2 and 26.5% in ROGUE-3 score over benchmark studies. We also analyze factual consistency scores while preserving the medical context. Our code is publicly available at https://github.com/fahmidahossain/Report_Summarization.

Large language models (LLMs) are possessed of numerous beneficial capabilities, yet their potential inclination harbors unpredictable risks that may materialize in the future. We hence propose CRiskEval, a Chinese dataset meticulously designed for gauging the risk proclivities inherent in LLMs such as resource acquisition and malicious coordination, as part of efforts for proactive preparedness. To curate CRiskEval, we define a new risk taxonomy with 7 types of frontier risks and 4 safety levels, including extremely hazardous,moderately hazardous, neutral and safe. We follow the philosophy of tendency evaluation to empirically measure the stated ”desire” of LLMs via fine-grained multiple-choice question answering. The dataset consists of 14,888 questions that simulate scenarios related to predefined 7 types of frontier risks. Each question is accompanied with 4 answer choices that state opinions or behavioral tendencies corresponding to the question. All answer choices are manually annotated with one of the defined risk levels so that we can easily build a fine-grained frontier risk profile for each assessed LLM. Extensive evaluation with CRiskEval on a spectrum of prevalent Chinese LLMs has unveiled a striking revelation: most models exhibit risk tendencies of more than 40% (weighted tendency to the four risk levels). Furthermore, a subtle increase in the model’s inclination toward urgent self-sustainability, power seeking and other dangerous goals becomes evident as the size of models increases. To promote further research on the frontier risk evaluation of LLMs, we publicly release our dataset at https://github.com/tjunlp-lab/CRiskEval.

Code benchmarks such as HumanEval are widely adopted to evaluate Large Language Models’ (LLMs) coding capabilities. However, there is an unignorable programming language bias in existing code benchmarks – over 95% code generation benchmarks are dominated by Python, leaving the LLMs’ capabilities in other programming languages such as Java and C/C++ unknown. Moreover, coding task bias is also crucial. Most benchmarks focus on code generation capability, while benchmarks for code reasoning (given input, reasoning output; and given output, reasoning input), an essential coding capability, are insufficient. Yet, constructing multi-lingual benchmarks can be expensive and labor-intensive, and codes in contest websites such as Leetcode suffer from data contamination during training. To fill this gap, we propose CRUXEVAL-X, a multi-lingual code reasoning benchmark that contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. In particular, the construction pipeline of CRUXEVAL-X works in a fully automated and test-guided manner, which iteratively generates and repairs based on execution feedback. Also, to cross language barriers (e.g., dynamic/static type systems in Python/C++), we formulated various transition rules between language pairs to facilitate translation. Our intensive evaluation of 24 representative LLMs reveals the correlation between language pairs. For example, TypeScript and JavaScript show a significant positive correlation, while Racket has less correlation with other languages. More interestingly, even a model trained solely on Python can achieve at most 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs.

Large language models (LLMs) have shown great potential in natural language processing tasks, but their application to machine translation (MT) remains challenging due to pretraining on English-centric data and the complexity of reinforcement learning from human feedback (RLHF). Direct Preference Optimization (DPO) has emerged as a simpler and more efficient alternative, but its performance depends heavily on the quality of preference data. To address this, we propose Confidence-Reward driven Preference Optimization (CRPO), a novel method that combines reward scores with model confidence to improve data selection for fine-tuning. CRPO selects challenging sentence pairs where the model is uncertain or underperforms, leading to more effective learning. While primarily designed for LLMs, CRPO also generalizes to encoder-decoder models like NLLB, demonstrating its versatility. Empirical results show that CRPO outperforms existing methods such as RS-DPO, RSO and MBR score in both translation accuracy and data efficiency.

Large language models (LLMs) excel in generating unstructured text. However, they struggle with producing structured output while maintaining accuracy in zero-shot information extraction (IE), such as named entity recognition (NER) and relation extraction (RE). To address these challenges, we propose CROSSAGENTIE, a multi-agent framework that enhances zero-shot IE through multi-agent LLM collaboration. CROSSAGENTIE refines LLM predictions iteratively through two mechanisms: intra-group cross-type debate, which resolves entity-label conflicts through context-based evidence and confidence aggregation, and inter-group cross-task debate, where NER and RE mutually refine outputs via bidirectional feedback. Furthermore, we introduce template fine-tuning, distilling high-confidence multi-agent outputs into a single model, significantly reducing inference cost while preserving accuracy. Experiments across five NER and five RE datasets show that CROSSAGENTIE significantly outperforms state-of-the-art zero-shot baselines by a large margin. CROSSAGENTIE effectively addresses LLMs limitations in structured prediction with an effective and efficient approach for zero-shot information extraction.

The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and thecomplexities of constructing tasks and evaluators. To overcome these limitations, we introduce CRAB, the first cross-environment agent benchmark framework, incorporating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface. Leveraging CRAB, we develope CRAB Benchmark-v0 comprising 120 tasks in computer desktop and mobile phone environments. We evaluated 6 advanced MLMs using different single and multi-agent system configurations on this benchmark. The experimental results demonstrate that the single agent with GPT-4o achieves the best completion ratio of 38.01%.

We introduce the concept of context-driven over-refusal, an abstention arising when model’s safety guardrails are triggered by the grounding knowledge provided alongside the user’s request. Distinct from question-driven over-refusal, this occurs in both retrieval-augmented generation (RAG) and natural language processing (NLP) task completion (e.g. summarization, translation) where external content can unexpectedly trigger refusals. In this work, we present a novel two-stage evaluation framework named COVER, designed to quantify and analyze this behavior. Through a comprehensive empirical study on two public corpora, we show that over-refusal rates strongly depend on the task, system prompts, model family, and the number of retrieved documents. We observe that tasks such as translation and summarization yield disproportionately high over-refusal rates, while question-answering remains relatively robust, especially in newer models. Moreover, increasing the number of contextual documents tends to reduce refusals, yet broadens the pool of prompts at risk of encountering at least one “unsafe” text. Interestingly, strict system prompts do not necessarily lead to higher over-refusal rates, suggesting that in the absence of explicit directives, some models may default to a more cautious behavior. These findings highlight the need for fine-grained alignment and benchmarking strategies sensitive to both user intent and contextual nuances, offering a roadmap for future research in model training and evaluation.

Despite progress in comment-aware multimodal and multilingual summarization for English and Chinese, research in Indian languages remains limited. This study addresses this gap by introducing COSMMIC, a pioneering comment-sensitive multimodal, multilingual dataset featuring nine major Indian languages. COSMMIC comprises 4,959 article-image pairs and 24,484 reader comments, with ground-truth summaries available in all included languages. Our approach enhances summaries by integrating reader insights and feedback. We explore summarization and headline generation across four configurations: (1) using article text alone, (2) incorporating user comments, (3) utilizing images, and (4) combining text, comments, and images. To assess the dataset’s effectiveness, we employ state-of-the-art language models such as LLama3 and GPT-4. We conduct a comprehensive study to evaluate different component combinations, including identifying supportive comments, filtering out noise using a dedicated comment classifier using IndicBERT, and extracting valuable insights from images with a multilingual CLIP-based classifier. This helps determine the most effective configurations for natural language generation (NLG) tasks. Unlike many existing datasets that are either text-only or lack user comments in multimodal settings, COSMMIC uniquely integrates text, images, and user feedback. This holistic approach bridges gaps in Indian language resources, advancing NLP research and fostering inclusivity.

Large Language Models encode behaviors like refusal within their activation space, but identifying these behaviors remains challenging. Existing methods depend on predefined refusal templates detectable in output tokens or manual review. We introduce **COSMIC** (Cosine Similarity Metrics for Inversion of Concepts), an automated framework for direction selection that optimally identifies steering directions and target layers using cosine similarity, entirely independent of output text. COSMIC achieves steering effectiveness comparable to prior work without any prior knowledge or assumptions of a model’s refusal behavior such as the use of certain refusal tokens. Additionally, COSMIC successfully identifies refusal directions in adversarial scenarios and models with weak safety alignment, demonstrating its robustness across diverse settings.