Morocco

Women are twice as likely as men to face online harassment due to their gender. Despite recent advances in multimodal content moderation, most approaches still overlook the social dynamics behind this phenomenon, where perpetrators reinforce prejudices and group identity within like-minded communities. Graph-based methods offer a promising way to capture such interactions, yet existing solutions remain limited by heuristic graph construction, shallow modality fusion, and instance-level reasoning. In this work, we present MemeWeaver, an end-to-end trainable multimodal framework for detecting sexism and misogyny through a novel inter-meme graph reasoning mechanism. We systematically evaluate multiple visual-textual fusion strategies and show that our approach consistently outperforms state-of-the-art baselines on the MAMI and EXIST benchmarks, while achieving faster training convergence. Further analyses reveal that the learned graph structure captures semantically meaningful patterns, offering valuable insights into the relational nature of online hate.

EACL 2026 Main Conference

MemeWeaver: Inter-Meme Graph Reasoning for Sexism and Misogyny Detection

sexism

memes

multimodal

graphs

interpretability

multilingual

hate speech detection

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)*<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. 

Speech-LLM integration faces a temporal-semantic granularity gap: speech representations scale with temporal duration while text tokens scale with semantic content. Existing duration-based methods generate embeddings at fixed rates, creating distributional mismatch with LLM pre-training. We propose SEAM (Speech Encoder-Decoder Alignment Module), an encoder-decoder architecture employing variable-rate generation through cross-attention between speech features and text embeddings. SEAM produces embeddings at adaptive rates that closely match natural text distributions while preserving pre-trained knowledge by freezing both speech encoder and LLM. We introduce a multi-stage training strategy and First Token Guidance to improve initial token prediction. SEAM achieves competitive performance on LibriSpeech (2.6\%/5.2\% WER). More significantly, trained only on LibriSpeech (960h), SEAM achieves 4.7\% WER on cross-domain TED-LIUM-v2, demonstrating that integrating LLM's linguistic knowledge enables effective generalization beyond limited speech training data.

SEAM: Bridging the Temporal-Semantic Granularity Gap for LLM-based Speech Recognition

Large Language Models (LLMs) encode substantial factual knowledge, yet measuring and systematizing this knowledge remains challenging. Converting it into structured format—for example through recursive extraction approaches such as the GPTKB methodology (Hu et al., 2025b)—is still underexplored. Key open questions include whether such extraction can terminate, whether its outputs are reproducible, and how robust they are to variations. We systematically study LLM knowledge materialization using miniGPTKBs (domain-specific, tractable subcrawls), analyzing termination, reproducibility, and robustness across three categories of metrics: yield, lexical similarity, and semantic similarity. We experiment with four variations (seed, language, randomness, model) and three illustrative domains (from history, entertainment, and finance). Our findings show (i) high termination rates, though model-dependent; (ii) mixed reproducibility; and (iii) robustness that varies by perturbation type—high for seeds and temperature, lower for languages and models. These results suggest that LLM knowledge materialization can reliably surface core knowledge, while also revealing important limitations.

Foundations of LLM Knowledge Materialization: Termination, Reproducibility, Robustness

Large Language Models (LLMs) have become an increasingly important tool in research and society at large. While LLMs are regularly used all over the world by experts and lay-people alike, they are predominantly developed with English-speaking users in mind, performing well in English and other wide-spread languages while less-resourced languages such as Luxembourgish are seen as a lower priority. This lack of attention is also reflected in the sparsity of available evaluation tools and datasets. In this study, we investigate the viability of language proficiency exams as such evaluation tools for the Luxembourgish language. We find that large models such as Claude and DeepSeek-R1 typically achieve high scores, while smaller models show weak performances. We also find that the performances in such language exams can be used to predict performances in other NLP tasks in Luxembourgish.

Testing Low-Resource Language Support in LLMs Using Language Proficiency Exams: the Case of Luxembourgish

Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages. One particularly valuable use case is generating synthetic samples that can be used to train smaller models in low-resource scenarios where human-labelled data is scarce. In this work, we investigate whether these synthetic data generation capabilities can serve as a form of distillation, producing smaller models that perform on par with or even better than massive LLMs across languages and tasks. To this end, we use a state-of-the-art multilingual LLM to generate synthetic datasets covering 11 languages and 4 classification tasks. These datasets are then used to train smaller models via fine-tuning or instruction tuning, or as synthetic in-context examples for compact LLMs. Our experiments show that even small amounts of synthetic data enable smaller models to outperform the large generator itself, particularly in low-resource languages. Overall, the results suggest that LLMs are best utilised as generators (teachers) rather than classifiers, producing data that empowers smaller and more efficient multilingual models.

Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification

Achieving pronunciation proficiency in a second language (L2) remains a challenge, despite the development of Computer-Assisted Pronunciation Training (CAPT) systems. Traditional CAPT systems often provide unintuitive feedback that lacks actionable guidance, limiting its effectiveness. Recent advancements in audio-language models (ALMs) offer the potential to enhance these systems by providing more user-friendly feedback. In this work, we investigate ALMs for chat-based pronunciation training by introducing \textbf{L2-Arctic-plus}, an English dataset with detailed error explanations and actionable suggestions for improvement. We benchmark cascaded ASR+LLMs and existing ALMs on this dataset, specifically in detecting mispronunciation and generating actionable feedback. To improve the performance, we further propose to instruction-tune ALMs on L2-Arctic-plus. Experimental results demonstrate that our instruction-tuned models significantly outperform existing baselines on mispronunciation detection and suggestion generation in terms of both objective and human evaluation, highlighting the value of the proposed dataset.

Unlocking Large Audio-Language Models for Interactive Language Learning

Question Answering on Tabular Data (or Table Question Answering) has seen tremendous advances with the coming of new generation Large Language Models (LLMs). Despite this, significant challenges still remain to be solved if we are to develop robust enough approaches for general usage. One of these is ambiguity in question answering, which historically has not merited much attention due to the previously limited capabilities of LLMs. In this work, we outlay the main types of ambiguousness inherent to tabular data. Then, we discuss how they are influenced by the way our models interact with the information stored in the tables, and we test the capabilities of some LLMs in detecting them. This work provides an initial ground for a deeper discussion on how to approach ambiguity in Tabular Data in the age of LLMs.

The Problem of Ambiguity in Table Question Answering

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.

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

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.

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SEAM: Bridging the Temporal-Semantic Granularity Gap for LLM-based Speech Recognition

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