Morocco

Reasoning language models have set state-of-the-art (SOTA) records on many challenging benchmarks, enabled by multi-step reasoning induced by reinforcement learning. However, reasoning models are prone to generating confident, plausible responses that are incorrect (hallucinations). Knowing when and how much to trust these models is critical for safe deployment in real-world applications. To this end, we explore uncertainty quantification (UQ) of reasoning models in this work. We ask three fundamental questions: First, are reasoning models well-calibrated? Second, does deeper reasoning improve model calibration? Finally, inspired by humans&#39; innate ability to double-check their thought processes to verify the validity of their answers and their confidence, we ask: can reasoning models improve their calibration by explicitly reasoning about their chain-of-thought traces? We introduce introspective uncertainty quantification (IUQ) to explore this direction. In extensive evaluations on SOTA reasoning models across a broad range of benchmarks, we find that reasoning models: (i) are typically overconfident, (ii) become even more overconfident with deeper reasoning, and (iii) can become better calibrated through introspection (e.g., o3-Mini and DeepSeek R1) but not uniformly (e.g., Claude 3.7 Sonnet becomes more poorly calibrated). We conclude with important research directions to design necessary UQ benchmarks and improve the calibration of reasoning models.

EACL 2026

Reasoning about Uncertainty: Do Reasoning Models Know When They Don&#39;t Know?

llm/ai agents

trustworthiness

uncertainty

calibration

reasoning

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. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Reasoning language models have set state-of-the-art (SOTA) records on many challenging benchmarks, enabled by multi-step reasoning induced by reinforcement learning. However, reasoning models are prone to generating confident, plausible responses that are incorrect (hallucinations). Knowing when and how much to trust these models is critical for safe deployment in real-world applications. To this end, we explore uncertainty quantification (UQ) of reasoning models in this work. We ask three fundamental questions: First, are reasoning models well-calibrated? Second, does deeper reasoning improve model calibration? Finally, inspired by humans' innate ability to double-check their thought processes to verify the validity of their answers and their confidence, we ask: can reasoning models improve their calibration by explicitly reasoning about their chain-of-thought traces? We introduce introspective uncertainty quantification (IUQ) to explore this direction. In extensive evaluations on SOTA reasoning models across a broad range of benchmarks, we find that reasoning models: (i) are typically overconfident, (ii) become even more overconfident with deeper reasoning, and (iii) can become better calibrated through introspection (e.g., o3-Mini and DeepSeek R1) but not uniformly (e.g., Claude 3.7 Sonnet becomes more poorly calibrated). We conclude with important research directions to design necessary UQ benchmarks and improve the calibration of reasoning models.

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?

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)*<br><br>

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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. 

Multimodal reasoning tasks such as visual question answering (VQA) require models to process both language and visual inputs. However, existing approaches typically decompose only language queries, treating images as monolithic inputs. We introduce REDI, a framework that jointly decomposes both images and questions into visual sub-domains (segmentation, material, depth, and color) with corresponding sub-questions. REDI employs an orchestrator agent that coordinates specialized worker agents, where each worker specializes in a specific visual sub-domain, and aggregates their responses through meta-reasoning. This hierarchical multi-agent design mitigates error propagation and improves compositional reasoning across both open- and closed-source MLLMs. On SEEDBench, MMBench, and CLEVR, REDI achieves absolute accuracy improvements of 8.9%, 8.2%, and 16.0%, respectively, marking a significant advancement in compositional multimodal reasoning. The code will be released on acceptance.

Compositional Reasoning via Joint Image and Language Decomposition

Identifying legal statutes given a situation is one of the most fundamental tasks in Legal NLP, and traditionally has been modeled using facts from court judgments as input, due to their abundance. However, in practical settings, the input queries are likely to be informal and asked by laymen, or non-professionals. This has led to the development of some layman LSI datasets, although there has been little research to explore the differences between court and layman data for LSI. In this work, we create ILSIC, a corpus of layman queries covering 500+ statutes from Indian law. Additionally, the corpus also contains court cases to enable researchers to effectively compare between court and layman data for LSI. We conducted extensive experiments on our corpus, including benchmarking over the layman dataset using zero and few-shot inference, retrieval-augmented generation and supervised fine-tuning. When comparing the different types of data, we observe that models trained purely on court queries are ineffective during test time on layman queries, while transfer learning from court to layman data can be beneficial in certain scenarios. We also conducted fine-grained analyses of our results in terms of categories of queries and frequency of statutes.

ILSIC: Corpora for Identifying Indian Legal Statutes from Queries by Laymen

Large language models (LLMs) are increasingly used as evaluators for natural language generation, applying human-defined rubrics to assess system outputs. However, human rubrics are often static and misaligned with how models internally represent language quality. We introduce GER-Eval (Generating Evaluation Rubrics for Evaluation) to investigate whether LLMs can design and use their own evaluation rubrics. We evaluate the semantic coherence and scoring reliability of LLM-defined criteria and their alignment with human criteria. LLMs reliably generate interpretable and task-aware evaluation dimensions and apply them within models, but their scoring reliability degrades in factual and knowledge-intensive settings. Closed-source models such as GPT-4o achieve higher agreement and cross-model generalization than open-weight models such as Llama. Our findings position evaluation as a learned linguistic capability of LLMsвЂ”consistent within models but fragmented across themвЂ”and call for new methods that jointly model human and LLM evaluative language to improve reliability and interpretability.

Learning to Judge: LLMs Designing and Applying Evaluation Rubrics

Federated fine‑tuning of foundation models is impeded by the need to communicate billions of parameters. Low‑rank adaptation (LoRA) alleviates this by updating only compact adapter matrices. However, varying client device capabilities lead to different adapter ranks, causing rank heterogeneity that undermines aggregation, and existing reconciliation methods still incur bias or inefficiency. To address this challenge, we propose RB-LoRA, a principled rank‑balanced aggregation framework that decomposes each update into rank‑wise components and aligns them using analytically derived weights. Experiments on both language and vision models demonstrate consistent improvements under one and three rounds of communication in federated learning.

RB-LoRA: Rank-Balanced Aggregation for Low-Rank Adaptation with Federated Fine-Tuning

Safety alignment aims to prevent Large Language Models (LLMs) from responding to harmful queries. To strengthen safety protections, jailbreak methods are developed to simulate malicious attacks and uncover vulnerabilities. In this paper, we introduce HILL (Hiding Intention by Learning from LLMs), a novel jailbreak approach that systematically transforms imperative harmful requests into learning-style questions with only straightforward hypotheticality indicators. Further, we introduce new metrics to thoroughly evaluate the efficiency and harmfulness of jailbreak methods. Experiments on the AdvBench dataset across a wide range of models demonstrate HILL's strong generalizability. It achieves top attack success rates on the majority of models and across malicious categories while maintaining high efficiency with concise prompts. Results of various defense methods show the robustness of HILL, with most defenses having mediocre effects or even increasing the attack success rates. In addition, the assessment of defenses on our constructed safe prompts reveals inherent limitations of LLMs' safety mechanisms and flaws in the defense methods. This work exposes significant vulnerabilities of safety measures against learning-style elicitation, highlighting a critical challenge of fulfilling both helpfulness and safety alignments.

A Simple and Efficient Learning-Style Prompting for LLM Jailbreaking

Modern logical reasoning with LLMs primarily relies on employing complex interactive frameworks that decompose the reasoning process into subtasks solved through carefully designed prompts or requiring external resources (e.g., symbolic solvers) to exploit their strong logical structures. While interactive approaches introduce additional overhead, hybrid approaches depend on external components, which limit their scalability. A non-interactive, end-to-end framework enables reasoning to emerge within the model itself---improving generalization while preserving analyzability without any external resources. In this work, we introduce a non-interactive, end-to-end framework for logical reasoning. We show that introducing structural information into the few-shot prompt activates a subset of attention heads that patterns aligned with logical reasoning operators. Building on this insight, we propose Attention-aware Intervention (AAI), a method that reweights attention scores across selected heads identified by their logical patterns. AAI offers an efficient way to steer the model's reasoning toward leveraging prior knowledge through attention modulation. Extensive experiments show that AAI enhances logical reasoning performance on three well-known benchmarks, including ProofWriter, PrOntoQA, and Logical Deduction, across model architectures and sizes while introducing no additional computational overhead.

Improving Chain-of-Thought for Logical Reasoning via Attention-Aware Intervention

Attribute-controlled translation (ACT) seeks to produce translations that satisfy specific constraints on linguistic and stylistic attributes. While careful prompt engineering can enable large language models to perform strongly in this task, its effectiveness is mainly limited to models of very large size. For this reason, in this paper we set to improve the performance of language models of more contained size by leveraging the contrastive nature of ACT tasks with preference optimization, as well as exploiting knowledge distillation with synthetically-generated training samples from larger models. As a resource for this investigation, we also introduce PREF-FAME-MT, a large, contrastive, formality-controlled parallel corpus which has been generated by expanding the existing FAME-MT dataset with synthetic contrastive samples. Experiments conducted over three datasets for formality- and gender-controlled translation with 71 distinct language pairs have demonstrated the effectiveness of the proposed approach at simultaneously improving attribute matching and translation quality. We release all our code and datasets to allow reproduction and expansion of our work.

Attribute-Controlled Translation with Preference Optimization

We introduce ReciFine, the largest human-evaluated, finely annotated recipe dataset to date, designed to advance controllable and trustworthy recipe generation. Existing resources, such as RecipeNLG, extract food items only from ingredient lists, overlooking entities expressed in instructions, including tools, chef actions, food and tool states, and durations, which are crucial for realistic and context-aware generation. To address this limitation, we extend RecipeNLG with finely annotated extraction of over 97 million entities across ten entity types from 2.2 million recipes. We are the first to explore recipe generation with explicit control over multiple entity types, enabling models to generate recipes conditioned not only on ingredients but also on tools, chef actions, cooking durations, and other contextual factors. Large language models fine-tuned or few-shot prompted with ReciFine extractions consistently outperform those trained on ingredient-list data alone across both automatic and human evaluations. ReciFine establishes a foundation for factual, coherent, structured, controllable recipe generation, and we release a human-annotated benchmark to support future evaluation and model development.

ReciFine: Finely Annotated Recipe Dataset for Controllable Recipe Generation

Recent advancements in Large Vision-Language Models (LVLMs) have shown groundbreaking capabilities across diverse multimodal tasks. However, these models remain vulnerable to adversarial jailbreak attacks, where adversaries craft subtle perturbations to bypass safety mechanisms and trigger harmful outputs. Existing white-box attacks methods require full model accessibility, suffer from computing costs and exhibit insufficient adversarial transferability, making them impractical for real-world, black-box settings. To address these limitations, we propose a black-box jailbreak attack on LVLMs via Zeroth-Order optimization using Simultaneous Perturbation Stochastic Approximation (ZO-SPSA). ZO-SPSA provides three key advantages: (i) gradient-free approximation by input-output interactions without requiring model knowledge, (ii) model-agnostic optimization without the surrogate model and (iii) lower resource requirements with reduced GPU memory consumption. We evaluate ZO-SPSA on three LVLMs, including InstructBLIP, LLaVA and MiniGPT-4, achieving the highest jailbreak success rate of 83.0% on InstructBLIP, while maintaining imperceptible perturbations comparable to white-box methods. Moreover, adversarial examples generated from MiniGPT-4 exhibit strong transferability to other LVLMs, with ASR reaching 64.18%. These findings underscore the real-world feasibility of black-box jailbreaks and expose critical weaknesses in the safety mechanisms of current LVLMs.

Crafting Adversarial Inputs for Large Vision-Language Models Using Black-Box Optimization

In this paper we explore where information is collected and how it is propagated throughout layers in large language models (LLMs). We begin by examining the surprising computational importance of punctuation tokens which previous work has identified as attention sinks and memory aids. Using intervention-based techniques, we evaluate the necessity and sufficiency of punctuation tokens across layers in GPT-2, DeepSeek, and Gemma. Our results show stark model-specific differences: for GPT-2, punctuation is both necessary and sufficient in multiple layers, while this holds far less in DeepSeek and not at all in Gemma. Extending beyond punctuation, we ask whether LLMs process different components of input (e.g., subjects, adjectives, punctuation, full sentences) by forming early static summaries reused across the network, or if the model remains sensitive to changes in these components across layers. We investigate whether different reasoning rules are processed differently by LLMs. In particular, through interchange intervention and layer-swapping experiments, we find that conditional statements (if, then), and universal quantification (for all) are processed very differently. Our findings offer new insight into the internal mechanisms of punctuation usage and reasoning in LLMs and have implications for interpretability and model analysis.

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