Morocco

Large Language Models (LLMs) are now state-of-the-art at summarization, yet the hidden notion of importance that drives their information selections remains hidden. We propose to address this by combining behavioral and computational analyses. Behaviorally, we generate length-controlled summaries and derive empirical importance distributions based on how often each information unit is selected. These reveal that LLMs converge on consistent importance patterns, sharply different from pre-LLM baselines, and that LLMs cluster more by size than by family. Computationally, we probe internal representations and find that middle layers strongly encode importance, and certain attention heads alone align well with empirical importance distributions. Together, these results provide initial insights about what LLMs prioritize in summarization and how this priority is internally represented, opening a path toward interpreting, and ultimately controlling, information selection in LLMs.

EACL 2026

What Matters to an LLM? Behavioral and Computational Evidences from Summarization

information importance

behavioral analysis

model interpretation

large language models

probing

summarization

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

poster

#### *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. 

Large language models (LLMs) achieve state-of-the-art performance in machine translation, but are also known to be sensitive to errors in user prompts. Given these models are largely trained on and respond best to prompts in standard English, this may affect the quality of LLM outputs for second language English speakers as well as real-world lay users, with potentially disproportionate effects on the former. We explore this effect by modeling a range of error types exhibited by such users, motivated by studies of L2 English, and quantifying their impact on LLM performance. We work with two related tasks: machine translation and machine translation evaluation. We find that LLMs-as-MT are brittle to natural spelling errors but not to errors at the phrasal level. However, the variance in quality caused by these errors is lower than the variance over the initial prompt choice, suggesting that ``perfect English'' for a given prompt is less important than choosing a good prompt. Since lay users and L2 speakers may use non-optimal prompts as well as display imperfect language skills, our work calls for increasing the resilience of model performance to both these phenomena to best serve a diverse user base, both from a robustness and fairness perspective.

How Important is ‘Perfect’ English for Machine Translation Prompts?

Practicing conversations with large language models (LLMs) presents a promising alternative to traditional in-person language learning. However, most LLMs generate text at a near-native level of complexity, making them ill-suited for beginner learners (CEFR: A1вЂ“A2). In this paper, we investigate whether controllable generation techniques can adapt LLM outputs to better support absolute beginners. We evaluate these methods through both automatic metrics and a user study with university-level learners of Japanese. Our findings show that while prompting alone fails, controllable generation techniques can successfully improve output comprehensibility for beginner speakers (from 40.4% to 84.3%). We further introduce a new token-level evaluation metric, Token Miss Rate (TMR), that quantifies the proportion of incomprehensible tokens per utterance and correlates strongly with human judgments. To support future research in AI-assisted language learning, we release our code, models, annotation tools, and dataset.

Toward Beginner-Friendly LLMs for Language Learning: Controlling Difficulty in Conversation

Instruction pre-training (IPT) has recently emerged as an effective intermediate stage between vanilla pre-training and post-training for large language models (LLMs). However, the optimal design of IPT corpora---such as the balance between raw and instruction-response data, languages, and task categories---remains unclear. We systematically study IPT corpus composition using a bilingual (English and Japanese) and multi-category (coding, general, math, and reasoning) instruction-response dataset. Through extensive IPT experiments across four base models, including both English-centric and bilingual LLMs, we find that: (1) more instruction-response data generally enhances model performance, particularly for models with large VPT budgets; (2) Japanese instruction data can improve English performance through cross-lingual transfer; and (3) the effectiveness of post-training varies across categories: coding performance is largely determined during IPT, while math and reasoning continue to improve during post-training.

Comprehensive Study of Bilingual and Multi-category Instruction Pre-training

Fact-checking on major platforms, such as X, Meta, and TikTok, is shifting from expert-driven verification to a community-based setup, where users contribute explanatory notes to clarify why a post might be misleading. An important challenge here is determining whether an explanation is helpful for understanding real-world claims and the reasons why, which remains largely underexplored in prior research. In practice, most community notes remain unpublished due to slow community annotation, and the reasons for helpfulness lack clear definitions. To bridge these gaps, we introduce the task of predicting both the helpfulness of explanatory notes and the reason for this. We present COMMUNITYNOTES, a large-scale multilingual dataset of 105k posts with user-provided notes and helpfulness labels. We further propose a framework that automatically generates and improves reason definitions via automatic prompt optimization, and integrate them into prediction. Our experiments show that the optimized definitions can improve both helpfulness and reason prediction. Finally, we show that the helpfulness information are beneficial for existing fact-checking systems.

COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations

Fine-tuning LLMs introduces many important behaviors, such as instruction following, safety alignment. This makes it crucial to study how fine-tuning changes models' internal mechanisms. Sparse autoencoders (SAEs) offer a powerful tool for interpreting neural networks by extracting concepts (features) represented in their activations. Previous work observed that SAEs trained on base models transfer effectively to instruction-tuned (chat) models, attributed to activation similarity. In this work, we propose *feature drift* as an alternative explanation: the feature space remains relevant, but the distribution of feature activations changes. In other words, fine-tuning recombines existing concepts rather than learning new ones. We validate this by showing base SAEs reconstruct both base and chat activations comparably despite systematic differences, with individual features exhibiting clear drift patterns. In a refusal behavior case study, we identify base SAE features that drift to activate on harmful instructions in chat models. Causal interventions using these features confirm that they mediate refusal. Our findings suggest that monitoring how existing features drift, rather than searching for entirely new features, may provide a more complete explanation of how fine-tuning changes model capabilities.

Feature Drift: How Fine-Tuning Repurposes Representations in LLMs

While the reasoning abilities of large language models (LLMs) continue to advance, it remains underexplored how such abilities vary across languages in multilingual LLMs and whether different languages generate distinct reasoning paths. In this work, we explore how reasoning knowledge in different languages complements one another. We propose a novel cross-lingual reward modeling framework that aggregates reasoning signals across languages to enhance mathematical reasoning. Our results show a substantial improvement (about 10 scores) in multilingual mathematical reasoning performance compared to using reward modeling within a single language, benefiting even high-resource languages. While English often exhibits the highest performance in multilingual models, we find that cross-lingual sampling benefits English, particularly under low sampling budgets. Our findings reveal new opportunities to improve multilingual reasoning by leveraging the complementary strengths of diverse languages.

Best-of-L: Cross-Lingual Reward Modeling for Mathematical Reasoning

Effectively identifying threats and mitigating their potential damage during crisis situations, such as natural disasters or violent attacks, is paramount for safeguarding endangered individuals. To tackle these challenges, AI has been used in assisting humans in emergency situations. Still, the use of NLP techniques remains limited, and mostly focuses on classification tasks. The significant potential of timely warning message generation using NLG architectures, however, has been largely overlooked. In this paper we present CrisiText, the first large-scale dataset for the generation of warning messages across 13 different types of crisis scenarios. The dataset contains more than 400,000 warning messages (spanning almost 18,000 crisis situations) aimed at assisting civilians during and after such events. To generate the dataset, we started from existing crisis descriptions, and created chains of events related to the scenarios. Each event was then paired with a warning message. The generations follow expert's written guidelines to ensure correct terminology and factuality of their suggestions. Additionally, each message is accompanied by three suboptimal warning types to allow for the study of different NLG approaches. To this end, we conducted a series of experiments comparing supervised fine-tuning setups with preference alignment, zero-shot, and few-shot approaches. We further assessed model performance in out-of-distribution scenarios and evaluated the effectiveness of an automatic post-editor.

CrisiText: A dataset of warning messages for LLM training in emergency communication

Sparse Autoencoders (SAEs) have been successfully used to probe Large Language Models (LLMs) and extract interpretable concepts from their internal representations. These concepts are linear combinations of neuron activations that correspond to human-interpretable features. In this paper, we investigate the effectiveness of SAE-based explainability approaches for sentence classification, a domain where such methods have not been extensively explored. We present a novel SAE-based model \verb|ClassifSAE| tailored for text classification, leveraging a specialized classifier head and incorporating an activation rate sparsity loss. We benchmark this architecture against established methods such as ConceptShap, Independent Component Analysis, HI-Concept and a standard TopK-SAE baseline. Our evaluation covers several classification benchmarks and backbone LLMs. We further enrich our analysis with two novel metrics for measuring the precision of concept-based explanations, using an external sentence encoder. Our empirical results show that \verb|ClassifSAE| improves both the causality and interpretability of the extracted features.

Unveiling Decision-Making in LLMs for Text Classification : Extraction of influential and interpretable concepts with Sparse Autoencoders

Despite significant progress in natural image editing with state-of-the-art MLLMs, compositional layout and content editing for structured visual domains (e.g., posters, websites) remains underexplored. In this work, we introduce SMART-EDITOR, a multi-agent framework for compositional editing for structured images like posters or websites. Unlike prior models that focus on isolated local edits, SMART-EDITOR maintains global coherence through two complementary strategies: Reward-Refine, an inference-time reward-guided refinement method, and RewardDPO, a training-time preference optimization approach leveraging reward-aligned layout pairs. To evaluate performance, we introduce SMARTEdit-Bench, a benchmark of cascading multi-step edit instructions that are implicit in nature yet require layout and semantic-consistency preserving reasoning about edit order to preserve spatial and semantic consistency. Both automatic and human evaluations confirm the value of reward-guided planning in producing semantically consistent and visually coherent edits, beyond what single-shot VLMs can generate.

SMART-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity

Temporal Knowledge Graph (TKG) reasoning seeks to predict future missing facts from historical evidence. While diffusion models (DM) have recently gained attention for their ability to capture complex predictive distributions, two gaps remain: (i) the generative path is conditioned only on positive evidence, overlooking informative negative context, and (ii) training objectives are dominated by cross-entropy ranking, which improves candidate ordering but provides little supervision over the calibration of the denoised embedding. To bridge this gap, we introduce **N**egative-**A**ware **D**iffusion model for TKG **Ex**trapolation (**NADEx**). Specifically, NADEx encodes subject-centric histories of entities, relations and temporal intervals into sequential embeddings. NADEx perturbs the query object in the forward process and reconstructs it in reverse with a Transformer denoiser conditioned on the temporal-relational context. We further derive a cosine-alignment regularizer derived from batch-wise negative prototypes, which tightens the decision boundary against implausible candidates. Comprehensive experiments on four public TKG benchmarks demonstrate that NADEx delivers state-of-the-art performance.

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How Important is ‘Perfect’ English for Machine Translation Prompts?

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Behavioral Analysis of Information Salience in Large Language Models