Morocco

Large language models achieve strong performance through training on vast datasets. Can analogical paradigm organization enable lightweight models to match this performance with minimal data? We develop a computational approach implementing three cognitive-inspired principles: analogical structure, contrastive learning, and minimal contextual cues. We test this approach with structured completion tasks where models identify correct sentence completions from analogical patterns with contrastive alternatives. Training lightweight models (BERT+CNN, 0.5M parameters) on only one hundred structured examples of English causative/inchoative alternations achieves F1=0.95, outperforming zero-shot GPT-o3 (F1=0.87). Ablation studies confirm that analogical organization and contrastive structure improve performance, consistently surpassing randomly shuffled baselines across architectures. Cross-phenomenon validation using unspecified object alternations replicates these efficiency gains, confirming approach robustness. Our results show that analogical paradigm organization enables competitive linguistic rule learning with orders of magnitude less data than conventional approaches require.

EACL 2026 Main Conference

Analogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction

verb argument structures

sample-efficient learning

linguistic rule induction

analogical paradigm organization

cognitive-inspired input design

contrastive learning

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. 

Large language models (LLMs) are increasingly applied to tasks involving causal reasoning. However, current benchmarks often rely on string matching or surface-level metrics that fail to assess whether a model's output is formally valid under causal semantics. We propose DoVerifier, a symbolic verification framework that checks whether LLM-generated causal expressions are derivable from a given causal graph using rules from do-calculus and probability theory. This allows us to recover correct answers that would otherwise be marked incorrect due to superficial differences. Evaluations on synthetic data and causal QA benchmarks show that DoVerifier more accurately captures semantic correctness than standard metrics, offering a more rigorous and informative way to evaluate LLMs on causal tasks.

Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification

Detecting mental health crisis situations such as suicide ideation, rape, domestic violence, child abuse, and sexual harassment is a critical yet underexplored challenge for language models. When such situations arise during user–model interactions, models must reliably flag them, as failure to do so can have serious consequences. In this work, we introduce CRADLE BENCH, a benchmark for multi-faceted crisis detection. Unlike previous efforts that focus on a limited set of crisis types, our benchmark covers seven types defined in line with clinical standards and is the first to incorporate temporal labels. Our benchmark provides 600 clinician-annotated evaluation examples and 420 development examples, together with a training corpus of around 4K examples automatically labeled using a majority-vote ensemble of multiple language models, which significantly outperforms single-model annotation. We further fine-tune six crisis detection models on subsets defined by consensus and unanimous ensemble agreement, providing complementary models trained under different agreement criteria.Content warning: This paper discusses sensitive topics such as suicide ideation, self-harm, rape, domestic violence, and child abuse.

CRADLE Bench: A Clinician-Annotated Benchmark for Multi-Faceted Mental Health Crisis and Safety Risk Detection

Chain-of-Thought (CoT) prompting has emerged as a powerful technique for enhancing language model's reasoning capabilities. However, generating long and correct CoT trajectories is challenging. Recent studies have demonstrated that Looped Transformers, a standard Transformer with cross-block parameter-sharing architecture, possess remarkable length generalization capabilities, but their limited generality and adaptability prevent them from serving as an alternative to auto-regressive solutions. To better leverage the strengths of Looped Transformers, we propose **RELAY** (**RE**asoning through **L**oop **A**lignment iterativel**Y**). Specifically, we align the steps of Chain-of-Thought (CoT) reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers. This additional iteration-wise supervision not only preserves the Looped Transformer's ability for length generalization but also enables it to predict CoT reasoning steps for unseen data. Therefore, we leverage this Looped Transformer to generate accurate reasoning chains for complex problems that exceed the training length, which will then be used to fine-tune an auto-regressive model. We conduct extensive experiments, and the results demonstrate the effectiveness of our approach, with significant improvements in the performance of the auto-regressive model.

Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning

Medical multiple-choice question answering (MCQA) benchmarks report near-human accuracy, with some approaching saturation and fueling claims of clinical readiness. Yet a single accuracy score is a poor proxy for competence: models that change answers under minor perturbations cannot be considered reliable. We argue that reliability underpins accuracyвЂ“only consistent predictions make correctness meaningful. To address this, we release ReMedQA, a benchmark suite that augments three standard medical MCQA datasets with open-answer variants and systematically perturbed items. Building on this design, we introduce ReAcc and ReCon, two reliability metrics: ReAcc measures the proportion of questions answered correctly across all variations, while ReCon measures the proportion answered consistently regardless of correctness. Our evaluation shows that high MCQA accuracy masks low reliability: models remain sensitive to format and perturbation changes, and domain specialization offers no robustness gain. MCQA further underestimates smaller models while inflating large ones that exploit structural cuesвЂ“with some producing correct answers without seeing the question. These findings show that, despite near-saturated accuracy, we are not yet done with medical multiple-choice benchmarks.

ReMedQA: Are We Done With Medical Multiple-Choice Benchmarks?

Large Language Models (LLMs) struggle with syllogistic reasoning, frequently conflating content plausibility with logical validity. This well-known content effect undermines their capacity to act as reliable deductive reasoners, particularly in multilingual contexts where both linguistic variability and world knowledge may exacerbate biases. Prior work shows that prompting and tuning interventions can alleviate these issues only partially, leaving models vulnerable to semantic interference. To make reasoning more consistent, robust, and transferable across languages, we investigate the use of activation steeringвЂ”an inference-time intervention that modulates internal representations to suppress content effects. Our experiments demonstrate that steering techniques constructed for English-based syllogisms generalise effectively to multilingual datasets, yielding higher formal reasoning accuracy while minimally affecting language modelling performance. Moreover, steering supports partial transfer to out-of-distribution tasks, highlighting its potential as a scalable mechanism for content-invariant reasoning. These findings advance the prospect of developing LLMs that can serve as reliable soft reasoners across language landscapes.

Can Activation Steering Generalize Across Languages? A Study on Syllogistic Reasoning in Language Models

Tokenization plays a crucial role in the performance of language models. However, most existing tokenizers rely on frequency-based segmentation, which fails to capture the morphological structure of languages and often leads to inefficient token representations. In this study, we propose a novel tokenization method that emphasizes the importance of Korean morphological structures in eojeol (Korean spacing unit). This method is designed to accommodate both inter-eojeol segmentation and intra-eojeol segmentation, enabling the selection of subwords based on morphemes. We pretrained a language model using the proposed method and evaluated its performance on Korean benchmark tasks. Experimental results demonstrate that the proposed method generally outperforms existing approaches. Notably, it produces significantly fewer tokens per input sequence, indicating its effectiveness and efficiency for Korean language modeling.

Morpheme Matters: Morpheme-Based Subword Tokenization for Korean Language Models

In the global drive toward carbon neutrality, deeply coordinated smart energy systems underpin industrial transformation, yet their interdisciplinary, fragmented, and fast-evolving expertise prevents general-purpose LLMs, lacking domain knowledge and physical-constraint awareness, from delivering precise engineering-aligned inference and generation. To address these challenges, we introduce Helios, the first large language model tailored to the smart energy domain, together with a comprehensive suite of resources to advance LLM research in this field. Specifically, we develop Enersys, a multi-agent collaborative framework for end-to-end dataset construction, through which we produce: (1) the first smart energy knowledge base, EnerBase, to enrich the model's foundational expertise; (2) the first instruction fine-tuning dataset, EnerInsruct, to strengthen performance on domain-specific downstream tasks; and (3) the first RLHF dataset, EnerReinforce, to align the model with human preferences and industry standards. Leveraging these resources, Helios undergoes large-scale pretraining, SFT, and RLHF. We also release EnerBench, the first benchmark for evaluating LLMs in smart energy scenarios, and demonstrate that our approach significantly enhances domain knowledge mastery, task execution accuracy, and alignment with human preferences.

Helios: A Foundational Language Model for Smart Energy Knowledge Reasoning and Application

Evaluating Large Language Models (LLMs) for mental health support poses unique challenges to reliable evaluation due to the emotionally and cognitively complex nature of therapeutic dialogue. Existing benchmarks are limited in scale, authenticity, and reliability, often relying on synthetic or social media data, and lack frameworks to assess when automated judges can be trusted. To address the need for large-scale authentic dialogue datasets and judge-reliability assessment, we introduce two benchmarks that provide a framework for generation and evaluation in this domain. MentalBench-100k consolidates 10,000 authentic single-session therapeutic conversations from three real-world scenarios datasets, each paired with nine LLM-generated responses, yielding 100,000 response pairs. MentalAlign-70k reframes evaluation by comparing four high-performing LLM judges with human experts across 70,000 ratings on seven attributes, grouped into Cognitive Support Score (CSS) and Affective Resonance Score (ARS). We then employ the AffectiveвЂ“Cognitive Agreement Framework, a statistical methodology using intraclass correlation coefficients (ICC) with confidence intervals to quantify agreement, consistency, and bias between LLM judges and human experts. Our analysis reveals systematic inflation by LLM judges, strong reliability for cognitive attributes such as guidance and informativeness, reduced precision for empathy, and some unreliability in safety and relevance. Our contributions establish new methodological and empirical foundations for the reliable and large-scale evaluation of LLMs in mental health contexts.

When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation

VVision-Language Models excel in multi-modal tasks but often hallucinate objects or exhibit linguistic bias by over-repeating object names, especially in complex multi-object scenes. Existing methods struggle with multi-object grounding because language priors frequently dominate visual evidence, causing hallucinated or biased objects to produce attention distributions or similarity scores nearly indistinguishable from those of real objects. We introduce SHAPLENS, a Shapley value–based attribution framework using Kernel SHAP and multi-layer fusion to detect hallucinated and biased objects. Evaluated on ADE and COCO datasets across four leading VLMs, SHAPLENS improves hallucination detection accuracy by 8–12% and F1 by 10–14% over the best baselines. It also achieves up to 6% higher bias detection performance across three distinct bias types on a curated HQH benchmark and exhibits minimal degradation (<0.03%) across partial and perturbed contexts.

Attribution-Guided Multi-Object Hallucination and Bias Detection in Vision-Language Models

Claim verification is a core module in automated fact-checking systems, tasked with determining claim veracity using retrieved evidence. This work presents KG-CRAFT, a novel knowledge graph-based contrastive reasoning method that enhances automatic claim verification by LLMs. Our approach first constructs a knowledge graph from claims and associated reports, then formulates contextually relevant contrastive questions based on the knowledge graph structure. These questions guide the distillation of evidence-based reports, which are synthesised into a concise summary for veracity assessment. Extensive evaluations on two real-world datasets (LIAR-RAW and RAWFC) demonstrate that our method achieves a new state-of-the-art in predictive performance. Comprehensive analyses validate in detail the effectiveness of our knowledge graph-based contrastive reasoning approach in improving LLMs' fact-checking capabilities.

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Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification

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PRESENTATIONS

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