Morocco

Mathematical reasoning remains one of the most challenging domains for large language models (LLMs), requiring not only linguistic understanding but also structured logical deduction and numerical precision. While recent LLMs demonstrate strong general-purpose reasoning abilities, their mathematical competence across diverse languages remains underexplored. Existing benchmarks primarily focus on English or a narrow subset of high-resource languages, leaving significant gaps in assessing multilingual and cross-lingual mathematical reasoning. To address this, we introduce MATHMIST, a parallel multilingual benchmark for mathematical problem solving and reasoning. MATHMIST encompasses 2,890 parallel Bangla-English gold standard artifacts, totaling approximately 30K aligned question--answer pairs across thirteen languages, representing an extensive coverage of high-, medium-, and low-resource linguistic settings. The dataset captures linguistic variety, multiple types of problem settings, and solution synthesizing capabilities. We systematically evaluate a diverse suite of models, including open-source small and medium LLMs, proprietary systems, and multilingual-reasoning-focused models under zero-shot, chain-of-thought (CoT), perturbated reasoning, and code-switched reasoning paradigms. Our results reveal persistent deficiencies in LLMs&#39; ability to perform consistent and interpretable mathematical reasoning across languages, with pronounced degradation in low-resource settings. 

EACL 2026

MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning

multilingual benchmark

cross-lingual evaluation

mathematical reasoning

low-resource languages

code-switching

#### *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. 

Mathematical reasoning remains one of the most challenging domains for large language models (LLMs), requiring not only linguistic understanding but also structured logical deduction and numerical precision. While recent LLMs demonstrate strong general-purpose reasoning abilities, their mathematical competence across diverse languages remains underexplored. Existing benchmarks primarily focus on English or a narrow subset of high-resource languages, leaving significant gaps in assessing multilingual and cross-lingual mathematical reasoning. To address this, we introduce MATHMIST, a parallel multilingual benchmark for mathematical problem solving and reasoning. MATHMIST encompasses 2,890 parallel Bangla-English gold standard artifacts, totaling approximately 30K aligned question--answer pairs across thirteen languages, representing an extensive coverage of high-, medium-, and low-resource linguistic settings. The dataset captures linguistic variety, multiple types of problem settings, and solution synthesizing capabilities. We systematically evaluate a diverse suite of models, including open-source small and medium LLMs, proprietary systems, and multilingual-reasoning-focused models under zero-shot, chain-of-thought (CoT), perturbated reasoning, and code-switched reasoning paradigms. Our results reveal persistent deficiencies in LLMs' ability to perform consistent and interpretable mathematical reasoning across languages, with pronounced degradation in low-resource settings. 

technical paper

Large Language Models (LLMs) are increasingly used in creative tasks. However, a formal and systematic assessment of creativity capabilities in LLMs is still missing, which limits our understanding of how capable LLMs are in generating content that is diverse and contextually relevant. The widely used Divergent Association Task (DAT) focuses on novelty, ignoring appropriateness, a core component of creativity. We evaluate a range of state-of-the-art LLMs on DAT and show that their results on the task are lower than those of two baselines that do not possess any creative abilities, highlighting its critical shortcomings. Grounded in human creativity theory, which defines creativity as the combination of novelty and appropriateness, we introduce the Conditional Divergent Association Task (CDAT). CDAT conditions novelty on cue appropriateness, separating noise from creativity better than DAT, while remaining simple and objective. Under CDAT, smaller model families often show most creativity, whereas advanced families favor appropriateness at lower novelty; We hypothesize training and alignment likely shift models along this frontier, making outputs more appropriate but less creative. We will release the dataset and code to ensure reproducibility.

Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models

Emotion classification on social media is especially difficult when texts include informal, culturally grounded language like slang. Standard NLP benchmarks often miss these nuances, particularly in low-resource settings. We present SLANG-GraphRAG, a retrieval-augmented framework that integrates a culture-specific slang knowledge graph into large language models via one-shot prompting. Using multiple retrieval strategies, we incorporate slang definitions, regional usage, and conversational context. Our results show that incorporating structured cultural knowledge into the retrieval process leads to significant improvements, improving accuracy by up to 31% and F1 score by 28%, outperforming traditional and unstructured retrieval methods. To better evaluate model behavior, we propose a probabilistic metric that reflects the distribution of human annotations, providing a more nuanced measure of performance. This highlights the value of culturally sensitive applications and more balanced evaluation in subjective NLP tasks.

SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations

Neural audio codecs have recently enabled high-fidelity reconstruction at high compression rates, especially for speech. However, speech and non-speech audio exhibit fundamentally different spectral characteristics: speech energy concentrates in narrow bands around pitch harmonics (80-400 Hz), while non-speech audio requires faithful reproduction across the full spectrum, particularly preserving higher frequencies that define timbre and texture. This poses a challengeвЂ”speech-optimized neural codecs suffer degradation on music or sound. Treating the full spectrum holistically is suboptimal: frequency bands have vastly different information density and perceptual importance by content type, yet full-band approaches apply uniform capacity across frequencies without accounting for these acoustic structures. To address this gap, we propose **BSCodec** (Band-Split Codec), a novel neural audio codec architecture that splits the spectral dimension into separate bands and compresses each band independently. Experimental results demonstrate that BSCodec achieves superior reconstruction over baselines across sound and music, while maintaining competitive quality in the speech domain, when trained on the same combined dataset of speech, music and sound. Downstream benchmark tasks further confirm that BSCodec shows strong potential for use in downstream applications.

BSCodec: A Band-Split Neural Codec for High-Quality Universal Audio Reconstruction

Vision-Language Models (VLMs) have achieved remarkable progress in complex visual understanding across scientific and reasoning tasks. While performance benchmarking has advanced our understanding of these capabilities, the critical dimension of uncertainty quantification has received insufficient attention. Therefore, unlike prior conformal prediction studies that focused on limited settings, we conduct a comprehensive uncertainty benchmarking study, evaluating 16 state-of-the-art VLMs (open and closed-source) across 6 multimodal datasets with 3 distinct scoring functions. Our findings demonstrate that larger models consistently exhibit better uncertainty quantification; models that know more also know better what they don't know. More certain models achieve higher accuracy, while mathematical and reasoning tasks elicit poorer uncertainty performance across all models compared to other domains. This work establishes a foundation for reliable uncertainty evaluation in multimodal systems.

The Art of Saying "Maybe": A Conformal Lens for Uncertainty Benchmarking in VLMs

Data visualizations like charts are fundamental tools for quantitative analysis and decision-making across fields, requiring accurate interpretation and mathematical reasoning. The emergence of Multimodal Large Language Models (MLLMs) offers promising capabilities for automated visual data analysis, such as processing charts, answering questions, and generating summaries. However, they provide no visibility into which parts of the visual data informed their conclusions; this black-box nature poses significant challenges to real-world trust and adoption. In this paper, we take the first major step toward evaluating and enhancing the capabilities of MLLMs to attribute their reasoning process by highlighting the specific regions in charts and graphs that justify model answers. To this end, we contribute RADAR, a semi-automatic approach to obtain a benchmark dataset comprising 1000 charts, 2000 question-answer pairs, 3599 reasoning steps, and 11,220 attribution annotations. We also introduce a method that provides attribution for chart-based mathematical reasoning. Experimental results demonstrate that our reasoning-guided approach improves attribution accuracy by up to 15 percentage points compared to baseline methods, and enhanced attribution capabilities translate to stronger answer generation, achieving high semantic similarity (BERTScore 0.90) with ground truth responses. This advancement represents a significant step toward more interpretable and trustworthy chart analysis systems, enabling users to verify and understand model decisions through reasoning and attribution.

RADAR: A Reasoning-Guided Attribution Framework for Explainable Visual Data Analysis

The current state of event detection research has two notable re-occurring limitations that we investigate in this study. First, the unidirectional nature of decoder-only LLMs presents a fundamental architectural bottleneck for natural language understanding tasks that depend on rich, bidirectional context. Second, we confront the conventional reliance on Micro-F1 scores in event detection literature, which systematically inflates performance by favoring majority classes. Instead, we focus on Macro-F1 as a more representative measure of a model's ability across the long-tail of event types. Our experiments demonstrate that models enhanced with sentence context achieve superior performance over canonical decoder-only baselines. Using Low-Rank Adaptation (LoRA) during finetuning provides a substantial boost in Macro-F1 scores in particular, especially for the decoder-only models, showing that LoRA can be an effective tool to enhance LLMs' performance on long-tailed event classes.

Event Detection with a Context-Aware Encoder and LoRA for Improved Performance on Long-Tailed Classes

Restoring power distribution networks after disruptions demands rapid, reliable coordination across repair crews, mobile power sources, and switching actions under strict constraints. Classical optimization yields high-quality plans but can be slow, while reinforcement learning often requires feeder-specific training and careful reward shaping. We recast restoration as language-conditioned planning: a large language model generates high-level restoration plans over a compact pre-validated catalogue of feasible actions. This constrained generation design makes decisions reliably, scalably, and interpretably, and allows for real-time human-in-the-loop decision-making while requiring no topology-specific setup or retraining. Our method achieves near-mixed-integer-linear programming performance on the IEEE 13-node standard power distribution feeder and outperforms a time-capped MILP solver on the IEEE 33-node standard feeder by around 13\%, while using less than 1\% of its wall-clock runtime.

LARA: LLM-based Agile Power Distribution Network Restoration from Disastrous Events

Large Language Models (LLMs) are increasingly being considered for high-stakes decision-making, yet their application in statistical risk analysis remains largely underexplored. A central challenge in this domain is enabling LLMs to effectively leverage historical data. To address this, we propose novel methods for extracting key information from raw data and translating it into structured contextual input within the LLM prompt. Applying our methods to a case study of power outage risk assessment, we demonstrate that this contextualization strategy significantly improves the LLM's performance in risk assessment tasks. While the LLM's numerical predictions still do not match those of a standard machine learning model, the LLM-based approach offers distinct advantages in versatility and interpretability. These findings demonstrate a new paradigm for contextualizing data to support risk assessment.

How to Contextualize Empirical Data for Risk Analysis with LLMs: A Case Study of Power Outages

Interpretability in black-box dense retrievers remains a central challenge in Retrieval-Augmented Generation (RAG). Understanding how queries and documents semantically interact is critical for diagnosing retrieval behavior and improving model design. However, existing dense retrievers rely on static embeddings for both queries and documents, which obscures this bidirectional relationship. Post-hoc approaches such as re-rankers are computationally expensive, add inference latency, and still fail to reveal the underlying semantic alignment. To address these limitations, we propose Interpretable Modular Retrieval Neural Networks (IMRNNs), a lightweight framework that augments any dense retriever with dynamic, bidirectional modulation at inference time. IMRNNs employ two independent adapters: one conditions document embeddings on the current query, while the other refines the query embedding using corpus-level feedback from initially retrieved documents. This iterative modulation process enables the model to adapt representations dynamically and expose interpretable semantic dependencies between queries and documents. Empirically, IMRNNs not only enhance interpretability but also improve retrieval effectiveness. Across seven benchmark datasets, applying our method to standard dense retrievers yields average gains of +6.35% nDCG, +7.14% recall, and +7.04% MRR over state-of-the-art baselines. These results demonstrate that incorporating interpretability-driven modulation can both explain and enhance retrieval in RAG systems.

IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation

Automatic Speech Recognition (ASR) performance is heavily dependent on the availability of large-scale, high-quality datasets. For low-resource languages, existing open-source ASR datasets often suffer from insufficient quality and inconsistent annotation, hindering the development of robust models. To address these challenges, we propose a novel and generalizable data aggregation and preprocessing pipeline designed to construct high-quality ASR datasets from diverse, potentially noisy, open-source sources. Our pipeline incorporates rigorous processing steps to ensure data diversity, balance, and the inclusion of crucial features like word-level timestamps. We demonstrate the effectiveness of our methodology by applying it to Vietnamese, resulting in a unified, high-quality 500-hour dataset that provides a foundation for training and evaluating state-of-the-art Vietnamese ASR systems.

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Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models

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