Morocco

Large Language Models (LLMs) fail at reasoning when their parametric knowledge is outdated or incorrect. A common solution for mitigating outdated or incorrect information in models is to provide updated facts in-context or by knowledge editing, but such updates can conflict with LLMs&#39; parametric knowledge and limit their reasoning. Current benchmarks for this problem, however, largely focus only on single knowledge updates and fact recall in isolated scenarios. In this work, we introduce TКЂбґЂбґ„бґ‹ (*Testing Reasoning Amid Conflicting Knowledge*), a new benchmark for studying how LLMs propagate new knowledge through multi-step reasoning when it conflicts with the model&#39;s initial parametric knowledge. Spanning three reasoning-intensive scenarios (WIKI, CODE, and MATH), TКЂбґЂбґ„бґ‹ introduces multiple, realistic conflicts to mirror real-world complexity. Our results on TКЂбґЂбґ„бґ‹ reveal that providing updated facts yields limited performance gains and even worsens performance, compared with no facts provided. Performance further degrades with more updated facts. This failure stems from both inability to faithfully integrate updated facts and flawed reasoning even when knowledge is integrated. TКЂбґЂбґ„бґ‹ provides a rigorous new benchmark to measure and guide future progress on propagating conflicting knowledge in multi-step reasoning.

EACL 2026

Tracking the Limits of Knowledge Propagation: How LLMs Fail at Multi-Step Reasoning with Conflicting Knowledge

knowledge tracing/discovering/inducing

model editing

probing

reasoning

### Welcome!
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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Large Language Models (LLMs) fail at reasoning when their parametric knowledge is outdated or incorrect. A common solution for mitigating outdated or incorrect information in models is to provide updated facts in-context or by knowledge editing, but such updates can conflict with LLMs' parametric knowledge and limit their reasoning. Current benchmarks for this problem, however, largely focus only on single knowledge updates and fact recall in isolated scenarios. In this work, we introduce TКЂбґЂбґ„бґ‹ (*Testing Reasoning Amid Conflicting Knowledge*), a new benchmark for studying how LLMs propagate new knowledge through multi-step reasoning when it conflicts with the model's initial parametric knowledge. Spanning three reasoning-intensive scenarios (WIKI, CODE, and MATH), TКЂбґЂбґ„бґ‹ introduces multiple, realistic conflicts to mirror real-world complexity. Our results on TКЂбґЂбґ„бґ‹ reveal that providing updated facts yields limited performance gains and even worsens performance, compared with no facts provided. Performance further degrades with more updated facts. This failure stems from both inability to faithfully integrate updated facts and flawed reasoning even when knowledge is integrated. TКЂбґЂбґ„бґ‹ provides a rigorous new benchmark to measure and guide future progress on propagating conflicting knowledge in multi-step reasoning.

technical paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Understanding emotion from speech requires sensitivity to both lexical and acoustic cues. However, it remains unclear whether large audio language models (LALMs) genuinely process acoustic information or rely primarily on lexical contents. We present LISTEN (Lexical vs. Acoustic Speech Test for Emotion in Narratives), a controlled benchmark designed to disentangle lexical reliance from acoustic sensitivity in emotion understanding. Across evaluations of six state-of-the-art LALMs, we observe a consistent lexical dominance. Models predict вЂњneutralвЂќ when lexical cues are neutral or absent, show limited gains under cue alignment, and fail to classify distinct emotions under cue conflict. In paralinguistic settings, performance approaches chance. These results indicate that current LALMs largely вЂњtranscribeвЂќ rather than вЂњlisten,вЂќ relying heavily on lexical semantics while underutilizing acoustic cues. LISTEN offers a principled framework for assessing emotion understanding in multimodal models.

Do Audio LLMs Really LISTEN, or Just Transcribe? Measuring Lexical vs. Acoustic Emotion Cues Reliance

Recent advances in self-supervised learning (SSL) have led to powerful speech representation models, yet their robustness in real-world conversational settings remains largely untested. Most existing benchmarks focus on clean, single-speaker, and single-channel audio, failing to reflect the complexities of natural human interactionвЂ”where overlapping speech, background noise, and reverberation are the norm. To bridge these critical gaps, we present the Conversational Speech Processing Benchmark (CSPB), a new benchmark designed to assess the robustness of SSL speech models in realistic conversational scenarios. CSPB is constructed from four multi-party datasetsвЂ”AMI, AliMeeting, MMCSG, and DiPCoвЂ”and supports both single-channel and multi-channel evaluation. By releasing CSPB as an open-source toolkit, we aim to establish a unified framework for evaluating and advancing robust, spatially-aware self-supervised speech models.

CSPB: Conversational Speech Processing Benchmark for Self-supervised Speech Models

Text anonymization is a critical task for enabling research and development in high-stakes domains containing private data, like medicine, law, and social services. While much research has focused on redacting sensitive content from text, substantially less work has focused on what to replace redacted content with, which can enhance privacy and becomes increasingly important with greater levels of redaction. In this work, we formulate predicting replacements for sensitive spans as a research task with principled use-inspired evaluation criteria. We further propose a multi-token completion method for accomplishing this task that is designed to preserve consistency with low compute requirements, thus facilitating practitioners to anonymize data locally before sharing it externally. Human and automated annotations demonstrate that our approach produces more realistic text and better preserves utility than alternative infilling methods and differentially private mechanisms across multiple domains without retraining. Overall, our work explores the under-studied task of what to replace redacted content with and contributes grounded evaluations capturing utility, facilitating future work.

Multi-Token Completion for Text Anonymization

Large audio-language models increasingly operate on raw speech inputs, enabling more seamless integration across domains such as voice assistants, education, and clinical triage. This transition, however, introduces a distinct class of vulnerabilities that remain largely uncharacterized. We examine the security implications of this modality shift by designing a text-to-audio jailbreak that embeds disallowed directives within a narrative-style audio stream. The attack leverages an advanced instruction-following text-to-speech (TTS) model to exploit structural and acoustic properties, thereby circumventing safety mechanisms primarily calibrated for text. When delivered through synthetic speech, the narrative format elicits restricted outputs from state-of-the-art models, including Gemini 2.0 Flash, achieving a 98.26% success rate that substantially exceeds text-only baselines. These results highlight the need for safety frameworks that jointly reason over linguistic and paralinguistic representations, particularly as speech-based interfaces become more prevalent.

Now You Hear Me: Audio Narrative Attacks Against Large AudioвЂ“Language Models

Sparse autoencoders (SAEs) are commonly used to interpret the internal activations of large language models (LLMs) by mapping them to human-interpretable concept representations. While existing evaluations of SAEs focus on metrics such as the reconstruction-sparsity tradeoff, human (auto-)interpretability, and feature disentanglement, they overlook a critical aspect: the robustness of concept representations to input perturbations. We argue that robustness must be a fundamental consideration for concept representations, reflecting the fidelity of concept labeling. To this end, we formulate robustness quantification as input-space optimization problems and develop a comprehensive evaluation framework featuring realistic scenarios in which adversarial perturbations are crafted to manipulate SAE representations. Empirically, we find that tiny adversarial input perturbations can effectively manipulate concept-based interpretations in most scenarios without notably affecting the base LLM's activations. Overall, our results suggest that SAE concept representations are fragile and without further denoising or postprocessing they might be ill-suited for applications in model monitoring and oversight.

Evaluating Adversarial Robustness of Concept Representations in Sparse Autoencoders

Evaluating the quality of children's utterances in adult-child dialogue remains challenging due to insufficient context-sensitive metrics. Common proxies such as Mean Length of Utterance (MLU), lexical diversity (vocd-D), and readability indices (Flesch-Kincaid Grade Level, Gunning Fog Index) are dominated by length and ignore conversational context, missing aspects of response quality such as reasoning depth, topic maintenance, and discourse planning. We introduce an LLM-as-a-judge framework that first classifies the Previous Adult Utterance Type and then scores the child's response along two axes: Expansion (contextual elaboration and inferential depth) and Independence (the child's contribution to advancing the discourse). These axes reflect fundamental dimensions in child language development, where Expansion captures elaboration, clause combining, and causal and contrastive connectives. Independence captures initiative, topic control, decreasing reliance on adult scaffolding through growing self-regulation, and audience design. We establish developmental validity by showing age-related patterns and demonstrate predictive value by improving age estimation over common baselines. We further confirm semantic sensitivity by detecting differences tied to discourse relations. Our metrics align with human judgments, enabling large-scale evaluation. This shifts child utterance assessment from simply measuring length to evaluating how meaningfully the child's speech contributes to and advances the conversation within its context.

Beyond Length: Context-Aware Expansion and Independence as Developmentally Sensitive Evaluation in Child Utterances

Current evaluation of large language models (LLMs) overwhelmingly prioritizes accuracy; however, in real-world and safety-critical applications, the ability to abstain when uncertain is equally vital for trustworthy deployment. We introduce a unified benchmark and evaluation protocol for abstention in medical multiple-choice question answering (MCQA), integrating conformal prediction, adversarial question perturbations, and explicit abstention options. Our systematic evaluation of both open- and closed-source LLMs reveals that even state-of-the-art, high-accuracy models often fail to abstain when uncertain. Notably, providing explicit abstention options consistently increases model uncertainty and safer abstention, far more than input perturbations, while scaling model size or advanced prompting brings little improvement. These findings highlight the central role of abstention mechanisms for trustworthy LLM deployment and offer practical guidance for improving safety in high-stakes applications.

Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty

Artificial intelligence (AI) and large language models (LLMs) in healthcare require advanced clinical skills (CS), yet current benchmarks fail to evaluate these comprehensively. We introduce MedQA-CS, an AI-SCE framework inspired by medical education's Objective Structured Clinical Examinations (OSCEs), to address this gap. MedQA-CS evaluates LLMs through two instruction-following tasksвЂ”LLM-as-medical-student and LLM-as-CS-examinerвЂ”designed to reflect real clinical scenarios. Our contributions include developing MedQA-CS, a comprehensive evaluation framework with publicly available data and expert annotations, and providing the quantitative and qualitative assessment of LLMs as reliable judges in CS evaluation. Our experiments show that MedQA-CS is a more challenging benchmark for evaluating clinical skills than traditional multiple-choice QA benchmarks (e.g., MedQA). Combined with existing benchmarks, MedQA-CS enables a more comprehensive evaluation of LLMs' clinical capabilities for both open- and closed-source LLMs.

MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills

Large language models (LLMs) have shown strong capabilities as task-solving agents across interactive domains. However, in complex environments, these agents may need to rely on auxiliary guidance to reduce the search space or make up for limited domain-specific knowledge. Such guidance includes human-provided manuals and demonstrations, retrieved examples from memory or external tools, high-level heuristics, and agent-acquired knowledge from prior interactions. However, this guidance may be imperfect. For example, due to changes in the environment, ambiguous or simplified language, or retrieval errors from external sources, guidance can be incomplete, outdated, or contextually mismatched, potentially causing errors or failures during task execution. To address this, we introduce MIRAGE, a benchmark for MeasurIng Robustness of LLM Agents under Imperfect GuidancE. MIRAGE includes procedurally generated environments in navigation, cooking, and gaming, where both the environment and the auxiliary guidance vary in fidelity and relevance. We further extend MIRAGE to realistic web tasks via WebArena, using noisy or underspecified instructions extracted from demonstrations. Our findings reveal critical failure modes in current LLM agents and motivate future work on improving their robustness under imperfect guidance.

Beyond Blind Following: Evaluating Robustness of LLM Agents under Imperfect Guidance

Recent advances in LLMs offer new opportunities for scalable, interactive mental health assessment, but excessive querying burdens users and is inefficient for real-world screening across transdiagnostic symptom profiles. We introduce MAQuA, a multi-outcome modeling and adaptive question-asking framework for simultaneous, multidimensional mental health screening. Combining multi-outcome modeling on language responses with item response theory (IRT) and factor analysis, MAQuA selects the questions with most informative responses across multiple dimensions at each turn to optimize diagnostic information, improving accuracy and potentially reducing response burden. Empirical results on a novel dataset reveal that MAQuA reduces the number of assessment questions required for score stabilization by 50вЂ“87% compared to random ordering (e.g., achieving stable depression scores with 71% fewer questions and eating disorder scores with 85% fewer questions). MAQuA demonstrates robust performance across both internalizing (depression, anxiety) and externalizing (substance use, eating disorder) domains, with early stopping strategies further reducing patient time and burden. These findings position MAQuA as a powerful and efficient tool for scalable, nuanced, and interactive mental health screening, advancing the integration of LLM-based agents into real-world clinical workflows.

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