AACL 2025 论文列表

Discourse relation parsing plays a crucial role in uncovering the logical structure of text, yet existing corpora focus almost exclusively on general-domain genres, leaving specialized fields like engineering under-resourced. We introduce ENG‐DRB, the first PDTB‐style discourse relation corpus derived from transcripts of hands‐on engineering tutorial videos. ENG‐DRB comprises 11 tutorials spanning civil, mechanical, and electrical/electronics engineering (155 minutes total) with 1,215 annotated relations. Compared to general‐domain benchmarks, this dataset features a high proportion of explicit senses, dense causal and temporal relations, and frequent overlapping and embedded senses. Our benchmarking experiments underscore the dataset’s difficulty. A top parser (HITS) detects segment boundaries well (98.6% F1), but its relation classification is more than 11 F1 percentages lower than on the standard PDTB. In addition, state‐of‐the‐art LLMs (OpenAI o4‐mini, Claude 3.7, LLaMA‐3.1) achieve at best 41% F1 on explicit relations and less than 9% F1 on implicit relations, revealing systematic errors in temporal and causal sense detection. The dataset can be accessed at: https://doi.org/10.57967/hf/6895. Code to reproduce our results is available at: https://github.com/chengzhangedu/ENG-DRB.

The 3rd Generation Partnership Project (3GPP) produces complex technical specifications essential to global telecommunications, yet their hierarchical structure, dense formatting, and multi-modal content make them difficult to process. While Large Language Models (LLMs) show promise, existing approaches fall short in handling complex queries, visual information, and document interdependencies. We present TelcoAI, an agentic, multi-modal Retrieval-Augmented Generation (RAG) system tailored for 3GPP documentation. TelcoAI introduces section-aware chunking, structured query planning, metadata-guided retrieval, and multi-modal fusion of text and diagrams. Evaluated on multiple benchmarks—including expert-curated queries—our system achieves 87% recall, 83% claim recall, and 92% faithfulness, representing a 16% improvement over state-of-the-art baselines. These results demonstrate the effectiveness of agentic and multi-modal reasoning in technical document understanding, advancing practical solutions for real-world telecommunications research and engineering.

The use of large language models (LLMs) for evaluating outputs is becoming an increasingly effective and scalable approach. However, it remains uncertain whether this capability extends beyond task-specific evaluations to more general assessments of text quality, particularly in multilingual contexts. In this study, we introduce – MTQ-Eval – a novel framework for multilingual text quality evaluation. We automatically generate text quality preference data and train open-source base LLMs to align with ratings of high- and low-quality text. Our comprehensive evaluation across 115 languages demonstrates the improved performance of the proposed model. Additionally, we explore whether this enhanced ability to distinguish between high- and low-quality text translates to better performance in downstream tasks.

While majority of current resources rely on formal text corrections, our work shifts the focus to non-native spoken Hindi error correction, which presents unique challenges due to its rich morphology, complex syntax, and distinct error patterns. To address the scarcity of authentic learner data, we introduce HiLearners, a dataset gathered from 2,500 real non-native Hindi speakers across three linguistic backgrounds (English, Bengali, Dravidian), capturing authentic error patterns including transfer errors, overgeneralization patterns, and contextual agreement issues. Furthermore, to overcome data resource limitations, we develop a methodical synthetic data augmentation technique, utilizing Large Language Models (LLMs) with a pattern analysis and controlled generation approach similar to Retrieval-Augmented Generation (RAG), yielding 5,500 carefully verified synthetic examples. Through extensive experiments on individual, mixed, and progressive curriculum-based configurations using multilingual models, we demonstrate that LLM-based synthetic data combined with three-phase curriculum learning significantly boosts performance, achieving a 76.92 GLEU score and surpassing human-only baselines. This work bridges the gap between native-centric error correction research and non-native Hindi learner needs, establishing a realistic assessment standard for advancing low-resource language processing.

Despite Telugu being spoken by over 80 million people, speech translation research for this morphologically rich language remains severely underexplored. We address this gap by developing a high-quality Telugu-English speech translation benchmark from 46 hours of manually verified CSTD corpus data (30h/8h/8h train/dev/test split). Our systematic comparison of cascaded versus end-to-end architectures shows that while IndicWhisper + IndicMT achieves the highest performance due to extensive Telugu-specific training data, fine-tuned SeamlessM4T models demonstrate remarkable competitiveness despite using significantly less Telugu-specific training data. This finding suggests that with careful hyperparameter tuning and sufficient parallel data (potentially less than 100 hours), end-to-end systems can achieve performance comparable to cascaded approaches in low-resource settings. While our metric reliability study evaluating BLEU, METEOR, ChrF++, ROUGE-L, TER, and BERTScore against human judgments reveals that traditional metrics provide better quality discrimination than BERTScore for Telugu–English translation. The work delivers three key contributions: a reproducible Telugu–English benchmark, empirical evidence of competitive end-to-end performance potential in low-resource scenarios, and practical guidance for automatic evaluation in morphologically complex language pairs.

Emotion recognition methods typically assign labels at the sentence level, obscuring the specific linguistic cues that signal emotion. This limits their utility in applications requiring targeted responses, such as empathetic dialogue and clinical support, which depend on knowing which language expresses emotion. The task of identifying emotion evidence – text spans conveying emotion – remains underexplored due to a lack of labeled data. Without span-level annotations, we cannot evaluate whether models truly localize emotion expression, nor can we diagnose the sources of emotion misclassification. We introduce the SEER (Span-based Emotion Evidence Retrieval) Benchmark to evaluate Large Language Models (LLMs) on this task. SEER evaluates single and multi-sentence span identification with new annotations on 1200 real-world sentences. We evaluate 14 LLMs and find that, on single-sentence inputs, the strongest models match the performance of average human annotators, but performance declines in multi-sentence contexts. Key failure modes include fixation on emotion keywords and false positives in neutral text.

As large reasoning models (LRMs) grow more capable, chain-of-thought (CoT) reasoning introduces new safety challenges. Existing SFT-based safety alignment studies dominantly focused on filtering prompts with safe, high-quality responses, while overlooking hard prompts that always elicit harmful outputs. To fill this gap, we introduce UnsafeChain, a safety alignment dataset constructed from hard prompts with diverse sources, where unsafe completions are identified and explicitly corrected into safe responses. By exposing models to unsafe behaviors and guiding their correction, UnsafeChain enhances safety while preserving general reasoning ability. We fine-tune three LRMs on UnsafeChain and compare them against recent SafeChain and STAR-1 across six out-of-distribution and five in-distribution benchmarks. UnsafeChain consistently outperforms prior datasets (21 wins out of 36 settings), with even a small selected-1K subset matching or surpassing baseline performance, demonstrating the effectiveness and generalizability of correction-based supervision.We release our dataset and code at https://github.com/mbzuai-nlp/UnsafeChain.

In this study, we evaluate the performance of large language models (LLMs) in detecting and correcting grammatical errors made by Chinese language learners. We find that incorporating various linguistic features—such as dependency structures, parts of speech, and pinyin transliteration—into the prompts can potentially enhance model performance. Among these features, parts of speech and pinyin prove to be the most effective across all tested models. Additionally, our findings show that the success of error correction also depends on the severity of the errors. When the intended meaning is preserved, LLMs tend to provide accurate revisions following the principle of minimal editing. However, when the meaning is obscured, LLMs are more likely to produce divergent outputs, both in comparison to reference corrections and to the responses of other models.

Large Language Models (LLMs) excel at providing information acquired during pretraining on large-scale corpora and following instructions through user prompts. However, recent studies suggest that LLMs exhibit biases favoring Western native English speakers over non-Western native speakers. Given English’s role as a global lingua franca and the diversity of its dialects, we extend this analysis to examine whether non-native English speakers also receive lower-quality or factually incorrect responses more frequently. We compare three groups—Western native, non-Western native, and non-native English speakers—across classification and generation tasks. Our results show that performance discrepancies occur when LLMs are prompted by the different groups for the classification tasks. Generative tasks, in contrast, are largely robust to nativeness bias, likely due to their longer context length and optimization for open-ended responses. Additionally, we find a strong anchoring effect when the model is made aware of the user’s nativeness for objective classification tasks, regardless of the correctness of this information. Our analysis is based on a newly collected dataset with over 12,000 unique annotations from 124 annotators, including information on their native language and English proficiency.

This work examines the potential of surprisal slope as a feature for identifying multi-word expressions (MWEs) in English, leveraging token-level surprisal estimates from the GPT-2 language model. Evaluations on the DiMSUM and SemEval-2022 datasets reveal that surprisal slope provides moderate yet meaningful discriminative power with a trade-off between specificity and coverage: while high recall indicates that surprisal slope captures many true MWEs, the slightly lower precision reflects false positives, particularly for non-MWEs that follow formulaic patterns (e.g., adjective-noun or verb-pronoun structures). The method performs particularly well for conventionalized expressions, such as idiomatic bigrams in the SemEval-2022 corpus. Both idiomatic and literal usages of these bigrams exhibit negative slopes, with idiomatic instances generally showing a more pronounced decrease.Overall, surprisal slope offers a cognitively motivated and interpretable signal that complements existing MWE identification methods, particularly for conventionalized expressions.

The integration of large language models (LLMs) into clinical medicine represents a major advancement in natural language processing (NLP). We introduce BioMistral-Clinical 7B, a clinical LLM built on BioMistral-7B (Labrak et al., 2024), designed to support continual learning from unstructured clinical notes for real-world tasks such as clinical decision support. Using the augmented-clinical-notes dataset provided by Hugging Face (2024), we apply prompt engineering to transform unstructured text into structured JSON, capturing key clinical information (symptoms, diagnoses, treatments, outcomes). This enables efficient incremental training via self-supervised continual learning (SPeCiaL) (Caccia and Pineau, 2021). Evaluation on MedQA (Jin et al., 2021) and MedMCQA (Pal et al., 2022) shows that BioMistral-Clinical 7B improves accuracy on MedMCQA by nearly 10 points (37.4% vs. 28.0%) over the base model, while maintaining comparable performance on MedQA (34.8% vs. 36.5%). Building on this, we propose the BioMistral-Clinical System, which integrates Retrieval-Augmented Generation (RAG) (Lewis et al., 2020) to enrich responses with relevant clinical cases retrieved from a structured vector database. The full system enhances clinical reasoning by combining domain-specific adaptation with contextual retrieval.

Test-time compute has emerged as a powerful paradigm in function-level code generation. However, previous proposed strategies have been viewed as disparate, thus lacking a fair apples-to-apples analysis enabling understanding of their operational mechanisms in execution-based benchmarks. Therefore, we present a mathematical framework that unifies generation and reranking with theoretical justifications through the lens of Minimum Bayes Risk (MBR) decoding. Our proposed framework leads to key research questions regarding the effectiveness of using parallel and/or iterative sampling, design choices of reranking signals and soft/hard MBR utility functions, and behaviors of the final selected program across different methods. Our empirical findings highlight the importance of the diversity of sampled candidates (over self-improvement), reranking with simple and high-quality signals, and the effectiveness of test-time compute to select programs that manifest general and edge test case robustness. We will open-source our analysis toolkit and implementation to enable reproducible research.We open-source our analysis toolkit and implementation to enable reproducible research.

Preference optimization is a critical post-training technique used to align large language models (LLMs) with human preferences, typically by fine-tuning on ranked response pairs. While methods like Direct Preference Optimization (DPO) have proven effective in English, they often fail to generalize robustly to multilingual settings. We propose a simple yet effective alternative, Confidence-Aware Preference Optimization (CAPO), which replaces DPO’s fixed treatment of preference pairs with a dynamic loss scaling mechanism based on a relative reward. By modulating the learning signal according to the confidence in each preference pair, CAPO enhances robustness to noisy or low-margin comparisons, typically encountered in multilingual text. Empirically, CAPO outperforms existing preference optimization baselines by at least 16% in reward accuracy, and improves alignment by widening the gap between preferred and dispreferred responses across languages.

Large language models (LLMs) sometimes hallucinate facts. Recent studies have shown that use of non-factual LLMs (anti-expert) have the potential to improve the factuality of the base LLM. Anti-expert methods penalize the output probabilities of the base LLM with an anti-expert LLM. Anti-expert methods are effective in mitigating hallucinations, but require high computational costs because the two LLMs are run simultaneously. In this paper, we propose an efficient anti-expert method called in-model anti-expert. It mitigated the hallucination problem with a single LLM and intervening to change the internal representations in the direction of improving factuality. Experiments results showed that the proposed method is less costly than the conventional anti-expert method and outperformed existing methods except for the anti-expert method. We confirmed that the proposed method improved GPU memory usage from 2.2x to 1.2x and latency from 1.9x to 1.2x.

We show that human players’ gameplay in the game of Wordle is influenced by the semantics, orthography, and phonology of the player’s previous guesses. We compare actual human players’ guesses with near-optimal guesses using NLP techniques. We study human language use in the constrained environment of Wordle, which is situated between natural language use and the artificial word association task.

This work considers a black-box threat model in which adversaries attempt to propagate arbitrary non-relevant content in search. We show that retrievers, rerankers, and LLM relevance judges are all highly vulnerable to attacks that enable arbitrary content to be promoted to the top of search results and to be assigned perfect relevance scores. We investigate how attackers may achieve this via content injection, injecting arbitrary sentences into relevant passages or query terms into arbitrary passages. Our study analyzes how factors such as model class and size, the balance between relevant and non-relevant content, injection location, toxicity and severity of injected content, and the role of LLM-generated content influence attack success, yielding novel, concerning, and often counterintuitive results. Our results reveal a weakness in embedding models, LLM-based scoring models, and generative LLMs, raising concerns about the general robustness, safety, and trustworthiness of language models regardless of the type of model or the role in which they are employed. We also emphasize the challenges of robust defenses against these attacks. Classifiers and more carefully prompted LLM judges often fail to recognize passages with content injection, especially when considering diverse text topics and styles. Our findings highlight the need for further research into arbitrary content injection attacks. We release our code for further study: https://github.com/manveertamber/content_injection_attacks.

Persuasion is a central feature of communication, widely used to influence beliefs, attitudes, and behaviors. In today’s digital landscape, across social media and online platforms, persuasive content is pervasive, appearing in political campaigns, marketing, fundraising appeals, and more. These strategies span a broad spectrum, from rational and ethical appeals to highly manipulative tactics, some of which pose significant risks to individuals and society. Despite the growing need to identify and differentiate safe from unsafe persuasion, empirical research in this area remains limited. To address this gap, we introduce SafePersuasion, a two-level taxonomy and annotated dataset that categorizes persuasive techniques based on their safety. We evaluate the baseline performance of three large language models in detecting manipulation and its subtypes, and report only moderate success in distinguishing manipulative content from rational persuasion. By releasing SafePersuasion, we aim to advance research on detecting unsafe persuasion and support the development of tools that promote ethical standards and transparency in persuasive communication online.

Difficult and unfamiliar concepts often hinder comprehension for lay audiences, especially in technical and educational domains. This motivates the usage of large language models (LLMs) for the process of text simplification (TS). In this work, we propose an iterative refinement framework that aims to simplify definitions by carefully handling complex terminology and domain-specific expressions. The obtained definition is reprocessed based on the critique, making refinements in successive iterations. We emphasize the use of small language models (SLMs) due to their faster response times and cost-efficient deployment. Human evaluations of the definitions produced at each refinement stage indicate consistent improvements in our specified evaluation criteria. We evaluate both LLM-as-a-judge score and human assessments along with automated metrics like BERTScore, BLEU-4, which provided supporting evidence for the effectiveness of our approach. Our work highlights the use of LLMs mimicking human-like feedback system in a TS task catering to a reader’s specific cognitive needs. Thus, we find that an iterative, critique-driven method can be an effective strategy for the simplification of dense or technical texts, particularly in domains where jargon impedes understanding.

Dysarthria, a motor speech disorder affecting over 46 million individuals globally, impairs both intelligibility and emotional expression in communication. This work introduces a novel framework for emotion-aware sentence reconstruction from dysarthric speech using Large Language Models (LLMs) fine-tuned with QLoRA, namely LLaMA 3.1 and Mistral 8x7B. Our pipeline integrates direct emotion recognition from raw audio and conditions textual reconstruction on this emotional context to enhance both semantic and affective fidelity.We propose the Multimodal Communication Dysarthria Score (MCDS), a holistic evaluation metric combining BLEU, semantic similarity, emotion consistency, and human ratings:MCDS=αB+βE+γS+δHwhere \alpha + \beta + \gamma + \delta = 1.On our extended TORGO+ dataset, our emotion-aware LLM model achieves a MCDS of 0.87 and BLEU of 72.4%, significantly outperforming traditional pipelines like Kaldi GMM-HMM (MCDS: 0.52, BLEU: 38.1%) and Whisper-based models. It also surpasses baseline LLM systems by 0.09 MCDS. This sets a new benchmark in emotionally intelligent dysarthric speech reconstruction, with future directions including multilingual support and real-time deployment.