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The transition to end-to-end Multimodal Large Language Models (MLLMs) has positioned these architectures as active social evaluators in high-stakes domains. However, it remains unclear whether these models maintain objective auditory perception or succumb to the &quot;Hearing with Eyes&quot; phenomenon, where visual racial cues distort linguistic proficiency evaluations. We investigate this cross-modal bias by constructing a controlled counterfactual dataset utilizing a Visual Matched-Guise Paradigm. By pairing identical native audio with diverse visual personas across English and Korean contexts, we reveal a distinct Cultural Asymmetry in model behavior. In Anglophone settings, most closed models exhibit Reverse Linguistic Stereotyping, hallucinating non-native accents for Asian speakers despite standard native audio. Conversely, in Korean settings, the same models assign baseline-relative competence premiums across all visual personas, with the largest gains for out-group (White/Black) speakers, consistent with Expectancy Violation Theory. Our findings demonstrate that MLLMs do not merely process sensory inputs but actively reproduce context-dependent sociolinguistic ideologies.

ACL 2026

Visual Interference in Speech Evaluation: Cultural Asymmetry and Cross-Modal Bias in MLLMs

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

The transition to end-to-end Multimodal Large Language Models (MLLMs) has positioned these architectures as active social evaluators in high-stakes domains. However, it remains unclear whether these models maintain objective auditory perception or succumb to the "Hearing with Eyes" phenomenon, where visual racial cues distort linguistic proficiency evaluations. We investigate this cross-modal bias by constructing a controlled counterfactual dataset utilizing a Visual Matched-Guise Paradigm. By pairing identical native audio with diverse visual personas across English and Korean contexts, we reveal a distinct Cultural Asymmetry in model behavior. In Anglophone settings, most closed models exhibit Reverse Linguistic Stereotyping, hallucinating non-native accents for Asian speakers despite standard native audio. Conversely, in Korean settings, the same models assign baseline-relative competence premiums across all visual personas, with the largest gains for out-group (White/Black) speakers, consistent with Expectancy Violation Theory. Our findings demonstrate that MLLMs do not merely process sensory inputs but actively reproduce context-dependent sociolinguistic ideologies.

poster

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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Large language models (LLMs) are increasingly explored as scalable tools for mental health counseling, yet evaluating their safety remains challenging due to the interactional and context-dependent nature of clinical harm. Existing evaluation frameworks predominantly assess isolated responses using coarse-grained taxonomies or static datasets, limiting their ability to diagnose how harms emerge and accumulate over multi-turn counseling interactions. In this work, we introduce R-MHSafe, a role-aware mental health safety taxonomy that characterizes clinically significant harm in terms of the interactional roles an AI counselor adopts, including perpetrator, instigator, facilitator, or enabler, combined with clinically grounded harm categories. Then, we propose MHSafeEval, a closed-loop, agent-based evaluation framework that formulates safety assessment as trajectory-level discovery of harm through adversarial multi-turn interactions, guided by role-aware modeling. Using R-MHSafe and MHSafeEval, we conduct a large-scale evaluation across state-of-the-art LLMs. Our results reveal substantial role-dependent and cumulative safety failures that are systematically missed by existing static benchmarks, and show that our framework significantly improves failure-mode coverage and diagnostic granularity.

MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models

Unit test generation has become a promising and important use case for Large Language Models (LLMs). However, existing evaluation benchmarks for LLM unit test generation primarily focus on function- or class-level code (single-file) rather than on more practical, challenging multi-file codebases.
To address this limitation, we propose \textbf{MultiFileTest}, a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. MultiFileTest features 20 high-quality, moderate-sized projects per language. We evaluate eleven frontier LLMs on MultiFileTest, and the results show that most tested LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark's inherent difficulty.
We also conduct a thorough error analysis, which shows that even advanced LLMs, such as Gemini 3.0 Pro, exhibit basic yet critical errors, including executability and cascade errors. Motivated by this observation, we further evaluate these frontier LLMs under manual error-fixing and self-error-fixing scenarios to assess their potential when equipped with error-fixing mechanisms.
Our dataset is available at 
\href{https://huggingface.co/datasets/yibowang214/MultiFileTest}{MultiFileTest}.

MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms

The ability of large language models (LLMs) to follow user instructions is central to their reliability, safety, and usefulness. While prior studies assess instruction adherence in the model’s main responses, we argue that it is also critical for large reasoning models (LRMs) to follow user instructions throughout their reasoning process. Reasoning instruction following makes LRMs more controllable and transparent, while reducing risks of undesirable shortcuts, hallucinations, or reward hacking within reasoning traces. To evaluate this dimension, we introduce ReasonIF, a systematic benchmark for assessing reasoning instruction following. ReasonIF includes six categories of instruction prompts, spanning multilingual reasoning, and length control. Across many open-source LRMs including GPT-OSS, Qwen3, and DeepSeek-R1, we find substantial failures in reasoning instruction adherence: the highest instruction following score (IFS) remains below 0.25, meaning that fewer than 25% of reasoning traces comply with the given instructions. Notably, as task difficulty increases, reasoning instruction following degrades further. We also explore two strategies to enhance reasoning instruction fidelity: (1) multi-turn reasoning and (2) Reasoning Instruction Finetuning (RIF) using synthetic data. RIF improves the IFS of GPT-OSS-20B from 0.11 to 0.27, indicating measurable progress but leaving ample room for improvement. We hope this work draws attention to reasoning-level instruction adherence as an underexplored but critical aspect of model alignment, and helps pave the way toward more controllable, interpretable, and trustworthy reasoning models.

ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning

Mixture-of-Experts (MoE) architectures have emerged as an effective approach for scaling Large Language Models (LLMs) by activating only a subset of experts during inference. Despite their computational efficiency, MoE models incur a substantial memory bottleneck from maintaining all expert parameters during inference. To address this challenge, numerous MoE pruning methods have been proposed. However, most existing methods adopt uniform pruning across layers, which fails to capture layer-wise variations in expert importance and redundancy. In this paper, we propose COmpensated MoE Pruning with Expert-Layer distribution (COMPEL). COMPEL performs layer-adaptive expert pruning by estimating expert importance using Fisher information and deriving layer importance from layer-wise outlier distributions, enabling pruning decisions that capture layer-wise heterogeneity. Furthermore, to mitigate performance degradation resulting from expert pruning, we propose a Fisher information guided expert weight compensation method. Experimental results on the Qwen1.5-MoE-A2.7B achieve near lossless performance at 25% expert pruning and maintains performance within a 4% margin even at 50% pruning. Moreover, COMPEL consistently outperforms existing pruning methods while substantially reducing inference latency and peak GPU memory usage.

 COMPEL: Compensated Mixture-of-Experts Pruning with Expert-Layer distribution

Large Language Models often struggle with complex, multi-step operational tasks because they remain static during inference and cannot learn from past experience. To address this, we propose MUSE, a framework that enables iterative self-improvement through a hierarchical Memory Module. MUSE organizes cross-domain insights to facilitate the orchestration of long-horizon workflows. The core of our approach is an autonomous post-execution critique mechanism: after completing each sub-task, the system analyzes its operational logs and distills raw execution data into structured, reusable knowledge. This allows the agent to evolve dynamically rather than relying on fixed parameters. Evaluated on the rigorous TAC productivity benchmark, MUSE achieves new state-of-the-art results, significantly outperforming previous methods using only the streamlined Gemini-2.5 Flash model. Our analysis demonstrates that MUSE’s performance scales with the accumulation of insights and exhibits strong cross-task transferability, marking a key step toward autonomous systems capable of lifelong learning in professional environments. Demo videos can be found in our supplementary materials.

Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience Refinement

Large language models (LLMs) are widely used in decision-making across diverse domains. Ensuring the generation of safe and reliable responses is critical for the effective deployment of LLM-based applications, particularly in high-stakes domains such as healthcare and finance. Most of these applications typically use carefully crafted prompts to guide response generation; however, the relationship between prompts and the reliability of LLM-generated responses is not yet fully understood. To address this gap, we propose a novel prompt-response concept model that explains the relationship between the amount of task-relevant information (informativeness) provided in the prompt and the LLM-generated response uncertainty by identifying four sources of response uncertainty: prompt underspecification, model quality, task variability, and semantic redundancy. We prove that response uncertainty decreases as prompt informativeness or model quality increases, mirroring the behavior of epistemic uncertainty in probabilistic models. Our experimental results on real-world datasets further validate our proposed model and corroborate the theoretical results.

Prompting the Unknown: Understanding Response Uncertainty in Large Language Models

Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However, little is known about the frequently used chat template’s effect on the calibration of conversational LLMs. In this work, we investigate the mechanisms driving this miscalibration by decoupling the effects of the post-training algorithm and the chat format. We find that, while instruction tuning fundamentally harms calibration, the chat template aggravates the issue through an “ownership bias” – models are significantly more confident in their *own* answers than in identical answers provided by a user. Extensive experiments across six recent open-weight LLMs, three benchmarks, and three confidence elicitation methods show that models assign up to 26% higher confidence to their own responses. Leveraging this insight, we propose a simple inference-time strategy: framing the model's answer as user input during confidence elicitation. This approach significantly reduces overconfidence and improves calibration by up to 26% without the need for retraining, narrowing the gap between base and instruction-tuned models.

Large Language Models Are Overconfident in Their Own Responses

Retrieval-Augmented Generation (RAG) is widely used for question answering over well-structured document corpora. However, a large amount of real-world problem-solving knowledge is captured in goal-oriented dialogues, where common ground misalignment between users and helpers gives rise to sparse, diffuse, and dynamically refined evidence that challenges standard RAG pipelines. We propose Structured Dialogue Refinement (SDR), a unified framework that adapts dialogue corpora for RAG at both the retrieval and generation stages without altering the underlying pipeline. Specifically, SDR introduces Dual Dialogue Querying for intent-aligned retrieval via issue-centric and solution-centric pseudo-documents, and Graph-Structured Dialogues coupled with a relevance-driven subgraph selection strategy to enable effective utilization of conversational evidence. We further adopt a nugget-based evaluation setup for dialogue-grounded RAG, enabling fine-grained analysis of retrieval coverage and grounded answer generation. Experiments demonstrate that SDR substantially improves both retrieval quality and grounded QA performance under dialogue-specific structural challenges.

Structured Dialogue Refinement: Building Retrieval-Augmented Question Answering on Goal-Oriented Dialogues

Prompt tuning has achieved remarkable progress in vision–language models (VLMs) and is recently being adopted for audio–language models (ALMs). However, its generalization ability in ALMs remains largely underexplored. We observe that conventional prompt tuning for ALMs also suffers from the Base–New Tradeoff, and we identify that this issue stems from the disrupted semantic structure of the embedding space. To address this issue, we propose Semantically Expanded Prompt Tuning (SEPT)—a plug-and-play framework that explicitly regularizes the prompt embedding space by incorporating semantic neighbors generated by large language models. SEPT introduces a novel semantic expansion loss with margin constraints that promote intra-class compactness and inter-class separability, thereby enhancing the semantic structure of the prompt embedding space. For comprehensive evaluation, we establish the first benchmark setup for prompt generalization in ALMs, covering both base-to-new generalization and cross-dataset transferability. Extensive experiments demonstrate that SEPT consistently improves generalization performance across multiple prompt tuning baselines, while maintaining computational cost during inference.

Generalizable Prompt Tuning for Audio-Language Models via Semantic Expansion

Memory systems often organize user-agent interactions as retrievable external memory and are crucial for long-running agents by overcoming the limited context windows of LLMs. However, existing memory systems invoke LLMs to process every incoming interaction for memory extraction, and such an eager memory consolidation scheme leads to substantial token consumption. To tackle this problem, we propose RecMem by rethinking when memory consolidation should be conducted. RecMem stores incoming interactions in a subconscious memory layer and encode them using lightweight embedding models for retrieval. LLMs are only invoked to extract episodic and semantic memory when sustained recurrence are observed for semantically similar interactions. Such recurrence-based consolidation works because these interactions correspond to a semantic cluster with rich information and thus are worth extraction and summarization. To improve accuracy, RecMem also incorporates a semantic refinement mechanism that recovers the fine-grained facts omitted by memory extraction. Experiments show that RecMem reduces the memory construction token cost of three SOTA memory systems by up to 87% while exceeding their accuracy.

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MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models

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