Morocco

Event Causality Identification (ECI) aims to identify causal relationships between events, which is essential for root cause analysis. While recent studies reveal that Large Language Models (LLMs) exhibit significant causal hallucination, a systematic evaluation of their document-level ECI performance across varied structural characteristics and a corresponding dataset is currently lacking. To fill this gap, we first construct a structure-controlled dataset to comprehensively assess their document-level ECI performance across texts with various structural characteristics that influence the causal behaviors in ECI. We find that different LLMs exhibit divergent causal bias across texts with varied structures, ranging from consistent hallucination or neglect to structure-dependent shifts between the two. To mitigate the bias, furthermore, we formulate ECI as a causal inference problem and propose a causality identification framework grounded in the potential outcomes and the HalpernвЂ“Pearl (HP) definition of actual causality theory. Experimental results demonstrate that our framework significantly reduces the causal bias associated with directly using LLMs on ECI, while also achieving superior performance.

EACL 2026

Mitigating Causal Bias in LLMs via Potential Outcomes Framework and Actual Causality Theory

llms

evaluation

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

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. 

In this paper, we introduce the Quebec-French Benchmark of Linguistic Minimal Pairs (QFrBLiMP), a corpus designed to evaluate the linguistic knowledge of large language models on prominent grammatical phenomena in French. QFrBLiMP consists of 1,761 minimal pairs annotated with 20 linguistic phenomena. Specifically, these minimal pairs have been created by manually modifying sentences extracted from an official online resource maintained by a QuГ©bec government institution. Each pair is annotated by twelve Quebec-French native speakers, who select the sentence they feel is grammatical amongst the two. These annotations are used to compare the competency of LLMs with that of humans. In addition, QFrBLiMP is the first dataset for targeted linguistic evaluations of LLMs in French, enabling a comparison of the linguistic competence of LLMs across different languages. We evaluate different French-based LLMs by observing the rate of higher probabilities assigned to the sentences of each minimal pair for each category. We find that while grammatical competence scales with model size, a clear hierarchy of difficulty emerges. All benchmarked models consistently fail on phenomena requiring deep semantic understanding, revealing a critical limitation and a significant gap compared to human performance on these specific tasks.

QFrBLiMP: a Quebec-French Benchmark of Linguistic Minimal Pairs

Large language models (LLMs) are often claimed to exhibit reasoning ability when supervised with chain-of-thought (CoT) traces. True reasoning, however, requires invariance: isomorphic problems should yield identical solutions regardless of superficial variation. We test this property by evaluating base and reasoning-optimized models, including LLaMA, Mistral, Qwen, GPT-OSS, and Deepseek, on GSM8K and MATH isomorphic variants of the same problem. All models exhibit notable drops under perturbation. To test whether training can induce invariance, we fine-tune models with PoT supervision under both concrete and abstract prompt formulations. PoT reduces surface instability but yields only marginal accuracy gains and does not transfer to CoT evaluation. These findings suggest PoT confers template-level consistency but fails to robustly ground operator semantics, yielding consistency without correctness. These results indicate that current LLM fine-tuning practices reinforce structural imitation rather than true reasoning.

Program-of-Thought Reveals LLM Abstraction Ceilings

Dense large language models (LLMs) face critical efficiency bottlenecks, as they rigidly activate all parameters regardless of input complexity. While existing sparsity methods (static pruning or dynamic activation) partially address this issue, they either lack adaptivity to contextual or model structural demands or incur prohibitive computational overhead. Inspired by the human brain’s dual-process mechanisms — predictive coding (N400) for backbone sparsity and structural reanalysis (P600) for complex contexts — we propose CLADA, a Cognitive-Load-Aware Dynamic Activation framework that synergizes statistical sparsity with semantic adaptability. Our key insight is that LLM activations exhibit two complementary patterns: 1. Global Statistical Sparsity driven by sequence-level prefix information, and 2. Local Semantic Adaptability modulated by cognitive load metrics (e.g., surprisal and entropy). CLADA employs a hierarchical thresholding strategy: a baseline derived from offline error-controlled optimization ensures over 40% sparsity, which is then dynamically adjusted using real-time cognitive signals. Evaluations across six mainstream LLMs and nine benchmarks demonstrate that CLADA achieves 20% average speedup with less than 2% accuracy degradation, outperforming Griffin (over 5% degradation) and TT (negligible speedup). Crucially, we establish the first formal connection between neurolinguistic event-related potential (ERP) components and LLM efficiency mechanisms through multi-level regression analysis (R2 = 0.17), revealing a sparsity--adaptation synergy. Requiring no retraining or architectural changes, CLADA provides a deployable solution for resource-aware LLM inference while advancing biologically inspired AI design.

Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs

Large language models (LLMs) are considered valuable Intellectual Properties (IP) due to the enormous computational cost of training, making their protection against malicious stealing or unauthorized deployment crucial. Despite efforts in watermarking and fingerprinting, existing methods either affect text generation or rely on white-box access, limiting practicality. To address this, we propose DuFFin, a novel Dual-Level Fingerprinting framework for black-box ownership verification. DuFFin jointly extracts trigger patterns and knowledge-level fingerprints to identify the source of a suspect model. We conduct experiments on diverse open-source models, including four popular base LLMs and their fine-tuned, quantized, and safety-aligned variants released by large companies, start-ups, and individuals. Results show that DuFFin accurately verifies the copyright of protected LLMs on their variants, achieving an IP-ROC greater than 0.99. Our code is available at https://anonymous.4open.science/r/llm-fingerprint-EC9F.

DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection

Complex claim verification requires decomposing sentences into verifiable subclaims, yet existing methods struggle to align decomposition quality with verification performance. We propose a reinforcement learning (RL) approach that jointly optimizes decomposition quality and verifier alignment using Group Relative Policy Optimization (GRPO). Our method integrates: (i) structured sequential reasoning; (ii) supervised finetuning on teacher-distilled exemplars; and (iii) a multi-objective reward balancing format compliance, verifier alignment, and decomposition quality. Across six evaluation settings, our trained 8B decomposer improves downstream verification performance to 71.75% macro-F1, outperforming prompt-based approaches (+1.99, +6.24) and existing RL methods (+5.84). Human evaluation confirms the high quality of the generated subclaims. Our framework enables smaller language models to achieve state-of-the-art claim verification by jointly optimising for verification accuracy and decomposition.

Distill and Align Decomposition for Enhanced Claim Verification

Large Language Models (LLMs) often default to overly cautious and vague responses when handling sensitive topics, sacrificing helpfulness for safety. Existing evaluation frameworks lack systematic methods to identify and address specific weaknesses in responses to sensitive topics, making it difficult to improve both safety and helpfulness simultaneously. To address this, we introduce FINEST, a FINE-grained response evaluation taxonomy for Sensitive Topics, which breaks down helpfulness and harmlessness into errors across three main categories: Content, Logic, and Appropriateness. Experiments on a Korean-sensitive question dataset demonstrate that our score- and error-based improvement pipeline, guided by FINEST, significantly improves the model responses across all three categories, outperforming refinement without guidance. Notably, score-based improvement---providing category-specific scores and justifications---yields the most significant gains, reducing the error sentence ratio for Appropriateness by up to 33.09%. This work lays the foundation for a more explainable and comprehensive evaluation and improvement of LLM responses to sensitive questions.

Improving LLM Responses to Sensitive Topics Through Fine-Grained Evaluation

Large language models (LLMs) are increasingly applied in clinical decision support, yet current evaluations rarely reveal whether their outputs reflect genuine medical reasoning or superficial correlations. We introduce DeVisE (Demographics and Vital signs Evaluation), a behavioral testing framework that probe fine-grained clinical understanding through controlled counterfactuals. Using intensive care unit (ICU) discharge notes from MIMIC-IV, we construct both raw (real-world) and template-based (synthetic) variants with single-variable perturbations in demographic (age, gender, ethnicity) and vital sign attributes. We evaluate five LLMs, spanning general-purpose and medical variants, under zero-shot and fine-tuned settings. Model behavior is analyzed through (1) input-level sensitivity, capturing how counterfactuals alter likelihoods, and (2) downstream reasoning, measuring their effect on predicted ICU length-of-stay. Zero-shot models exhibit more coherent and clinically aligned reasoning patterns, whereas fine-tuned ones are stabler but less sensitive to meaningful changes. Demographic factors subtly yet consistently shape predictions, emphasizing the need for fairness-aware evaluation in medical LLMs.

DeVisE: Towards the Behavioral Testing of Medical Large Language Models

Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEENA (also known as PersianMMMU), the first dataset designed to evaluate Persian VLMs across scientific, reasoning, and human-level understanding tasks. Our dataset comprises approximately 7,500 Persian and 3,000 English questions, covering a wide range of topics such as reasoning, mathematics, physics, diagrams, charts, and Persian art and literature. Key features of MEENA include: (1) diverse subject coverage spanning various educational levels, from primary to upper secondary school, (2) rich metadata, including difficulty levels and descriptive answers, (3) original Persian data that preserves cultural nuances, (4) a bilingual structure to assess cross-linguistic performance, and (5) a series of diverse experiments assessing various capabilities, including overall performance, the model's ability to attend to images, and its tendency to generate hallucinations. We hope this benchmark contributes to enhancing VLM capabilities beyond English.

MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment

Tokenizer adaptation plays an important role in transferring pretrained language models to new domains or languages. In this work, we address two complementary aspects of this process: vocabulary extension and pruning. The common approach to extension trains a new tokenizer on domain-specific text and appends the tokens that do not overlap with the existing vocabulary, which often results in many tokens that are unreachable or never used. We propose continued BPE training, a simple alternative that extends an existing tokenizer by continuing its merge operations on new data instead of retraining from scratch. Experiments across multiple languages and model families show that this approach improves tokenization efficiency and leads to better utilization of added vocabulary. We also introduce leaf-based vocabulary pruning, which removes redundant tokens while preserving model quality. Together, these methods provide practical tools for controlled vocabulary modification, and we release an open-source implementation supporting both Byte-level and SentencePiece BPE tokenizers.

Teaching Old Tokenizers New Words: Efficient Tokenizer Adaptation for Pretrained Models

Large language models (LLMs) have advanced code generation from single-function tasks to competitive-programming problems, but existing multi-agent solutions either rely on costly large-scale (> 30 B) models or collapse when downsized to small open-source models. We present MapCoder-Lite, a framework for distilling the complex reasoning of large, multi-agent coding systems into a single 7B model. Our contribution is a novel, three-pillar methodology that synergistically generates, refines, and encodes multi-agent knowledge: (i) pass-based trajectory distillation from strong LLMs fixes format fragility in retrieval and reduces failures in debugging, (ii) supervisor-guided correction with global feedback strengthens planning and coding agents, and (iii) agent-wise LoRA fine-tuning delivers memory-efficient specialisation. Comprehensive evaluation on xCodeEval, APPS, and CodeContests shows that MapCoder-Lite more than doubles xCodeEval accuracy (13.2% → 28.3%), eliminates all format failures, while reducing GPU memory and token-generation time by 4x compared to a 32B model. It also achieves over 10% gains on simpler coding benchmarks, demonstrating broad improvements beyond competitive programming. These results demonstrate that careful agent-wise fine-tuning unleashes high-quality multi-agent coding on a small language model. Our code is publicly available at https://github.com/aiha-lab/MapCoder-Lite.

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QFrBLiMP: a Quebec-French Benchmark of Linguistic Minimal Pairs

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