Morocco

In a rapidly evolving labor market, detecting and addressing emerging skill needs is essential for shaping responsive education and workforce policies. Online job advertisements (OJAs) provide a real-time view of changing demands, but require first retrieving skill mentions from unstructured text and then solving the entity linking problem of connecting them to standardized skill taxonomies. To harness this potential, we present a multilingual human-in-the-loop (HITL) pipeline that operates in two steps: candidate skills are extracted from national OJA corpora using country-specific word embeddings, capturing terms that reflect each country’s labor market. These candidates are linked to ESCO using an encoder-based system and refined through a decoder large language models (LLMs) for accurate contextual alignment. Our approach is validated through both quantitative and qualitative evaluations, demonstrating that our method enables timely, multilingual monitoring of emerging skills, supporting agile policy-making and targeted training initiatives.

EACL 2026

SkiLLens: Recognising and Mapping Novel Skills from Millions of Job Ads Across Europe Using Language Models

skill extraction

large language models

entity linking

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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EACL 2024

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

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)* 

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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. 

Automated structuring of medical prescriptions is critical for downstream safety checks in pharmacies, yet remains challenging due to heterogeneous layouts, OCR noise, and dense clinical abbreviations in real-world documents. Existing language models either ignore layout information, rely on computationally expensive image-based architectures, or cannot operate under strict privacy and hardware constraints such as GDPR and HDS-certified environments. We present a lightweight (<10M parameters), privacy-preserving transformer specifically designed for Entity Extraction (EE) and Entity Linking (EL) in French medical prescriptions. The model uses only OCR text and normalized 2D word coordinates, enabling robust pseudonymisation and real-time CPU-level inference while preserving essential spatial cues. It is pretrained on a large corpus of pseudonymised OCR outputs using objectives tailored to prescription structure, including a novel Token-to-Line Alignment (TLA) task, and fine-tuned on the Rx-PAD dataset (Pattin Cottet et al., 2025). Empirical results show that our approach matches or surpasses larger document-understanding models and rivals multimodal LLMs on strict extraction metrics, while achieving sub-second latency suitable for operational deployment. The system is currently used in 230 pharmacies, demonstrating both scalability and practical relevance. These findings highlight the importance of specialized, domain-aware, lightweight models for safe, efficient, and legally compliant prescription verification.

Lightweight Domain-Specific Language Model for Real-Time Structuring of Medical Prescriptions

The growing use of Large Language Models (LLMs) for medical Question Answering (QA) requires reliable, evidence-grounded benchmarks beyond English. In Italy, Riassunti delle Caratteristiche del Prodotto (RCP) issued by the Italian Medicines Agency (AIFA) are the main regulatory source on medicines, yet no QA dataset exists on these documents, limiting the development and evaluation of trustworthy Italian QA systems. We introduce \textbf{PharmaQA.IT}, an Italian extractive QA dataset built from RCPs in PharmaER.IT. Using a semi-automatic pipeline, we (i) select informative pages from 1{,}077 leaflets, (ii) prompt a multimodal LLM on page images with professional personas to generate candidate question–answer pairs, and (iii) validate and normalise them with expert revision. The final dataset contains 861 high-quality question–answer pairs on indications, contraindications, dosage, warnings, interactions, and pharmacological properties. We frame PharmaQA.IT as an extractive QA benchmark with structured JSON outputs and evaluate a range of open and proprietary LLMs. Results show that open models approach closed-source performance under a chunking-and-retrieval setup. PharmaQA.IT, together with all code, prompts, and evaluation scripts, will be publicly released to support research on trustworthy Italian biomedical QA. PharmaQA.IT, together with all code, prompts, and evaluation scripts, is publicly \href{https://huggingface.co/datasets/VillanovaAI/PharmaQA.IT}{available on Hugging Face} to support research on trustworthy Italian biomedical QA.

PharmaQA.IT: an Italian dataset for Q\&A in the pharmaceutical domain

Existing multilingual long-context benchmarks, often based on the popular needle-in-a-haystack test, primarily evaluate a model's ability to locate specific information buried within irrelevant texts. However, such a retrieval-centric approach is myopic and inherently limited, as successful recall alone does not indicate a model's capacity to reason over extended contexts. Moreover, these benchmarks are susceptible to data leakage, short-circuiting, and risk making the evaluation a priori identifiable. To address these limitations, we introduce MLRBench, a new synthetic benchmark for multilingual long-context reasoning. Unlike existing benchmarks, MLRBench goes beyond surface-level retrieval by including tasks that assess multi-hop inference, aggregation, and epistemic reasoning. Spanning seven languages, MLRBench is designed to be parallel, resistant to leakage, and scalable to arbitrary context lengths. Our extensive experiments with an open-weight large language model (LLM) reveal a pronounced gap between high- and low-resource languages, particularly for tasks requiring the model to aggregate multiple facts or predict the absence of information. We also find that, in multilingual settings, LLMs effectively utilise less than 30% of their claimed context length. Although off-the-shelf Retrieval Augmented Generation helps alleviate this to a certain extent, it does not solve the long-context problem. We open-source MLRBench to enable future research in improved evaluation and training of multilingual LLMs.

Can LLMs reason over extended multilingual contexts? Towards long-context evaluation beyond retrieval over haystacks

Training Large Language Models (LLMs) with high multilingual coverage is becoming increasingly important вЂ” especially when monolingual resources are scarce. Recent studies have found that LLMs process multilingual inputs in shared concept spaces, thought to support generalization and cross-lingual transfer. However, these prior studies often do not use causal methods, lack deeper error analysis or focus on the final model only, leaving open how these spaces emerge *during training*. We investigate the development of language-agnostic concept spaces during pretraining of EuroLLM through the causal interpretability method of activation patching. We isolate cross-lingual concept representations, then inject them into a translation prompt to investigate how consistently translations can be altered, independently of the language. We find that *shared concept spaces emerge early and continue to refine*, but that *alignment with them is language-dependent*. Furthermore, in contrast to prior work, our fine-grained manual analysis reveals that some apparent gains in translation quality reflect shifts in behavior вЂ” like selecting senses for polysemous words or translating instead of copying cross-lingual homographs вЂ” rather than improved translation ability. Our findings offer new insight into the training dynamics of cross-lingual alignment and the conditions under which causal interpretability methods offer meaningful insights in multilingual contexts.

When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training

Large Reasoning Models (LRMs) achieve strong performance on mathematical, scientific, and other question-answering tasks, but their multilingual reasoning abilities remain underexplored. When presented with non-English questions, LRMs often default to reasoning in English, raising concerns about interpretability and the handling of linguistic and cultural nuances. We systematically compare an LRM's reasoning in English versus the language of the question. Our evaluation spans two tasks: MGSM and GPQA Diamond. Beyond measuring answer accuracy, we also analyze cognitive attributes in the reasoning traces. We find that English reasoning traces exhibit a substantially higher presence of these cognitive behaviors, and that reasoning in English generally yields higher final-answer accuracy, with the performance gap increasing as tasks become more complex. However, this English-centric strategy is susceptible to a key failure mode - getting "Lost in Translation," where translation steps lead to errors that would have been avoided by reasoning in the language of the question.


The Reasoning Lingua Franca: A Double-Edged Sword for Multilingual AI

Document understanding requires modeling both structural and semantic relationships between the layout elements within the document, with human-perceived reading order (RO) playing a crucial yet often neglected role compared to heuristic OCR sequences used by most existing models. Previous approaches depend on costly, inconsistent human annotations, limiting scalability and generalization. To bridge the gap, we propose a cost-effective paradigm that leverages large language models (LLMs) to infer global RO and inter-element layout relations without human supervision. By explicitly incorporating RO as structural guidance, our method captures hierarchical, document-level dependencies beyond local adjacency. Experiments on Semantic Entity Recognition, Entity Linking, and Document Question Answering show consistent improvements over baseline methods. Notably, LLM-inferred RO, even when differing from ground-truth adjacency, provides richer global structural priors and yields superior downstream performance. These results and findings demonstrate the scalability and significance of RO-aware modeling, advancing both LLMs and lightweight layout-aware models for robust document understanding.

Rethinking Reading Order: Toward Generalizable Document Understanding with LLM-based Relation Modeling

Large language models (LLMs) have demonstrated strong performance across diverse natural language processing tasks. However, their performance varies significantly across different prompts, requiring careful engineering for consistent results. Manual prompt engineering requires substantial human effort and limits reproducibility, while automatic prompt optimization techniques, though reducing manual labor, rely on costly autoregressive generation and incur significant latency overheads. To address these limitations, we present low-latency prompt optimization (LLPO), a novel framework that reframes prompt engineering as a classification problem. LLPO classifies structured prompt fields from user input through multi-task classification and populates a predefined template to generate an optimized system prompt with minimal latency. In LLM-based automatic evaluations across four question-answering benchmarks, LLPO improves answer quality by up to 26.5% in в€†win rate compared to prior automatic prompt optimization methods, while reducing latency by up to 1,956 times. Human evaluation shows that LLPO receives the highest proportion of top-ranked responses. Furthermore, ablation studies analyze the contribution of each structured prompt field to performance, highlighting the robustness of our framework.

Don't Generate, Classify! Low-Latency Prompt Optimization with Structured Complementary Prompt

Much recent work has shown how cross-linguistic variation is constrained by competing pressures from efficient communication. However, little attention has been paid to the role of the systematicity of forms (regularity), a key property of natural language. Here, we demonstrate the importance of regularity in explaining the shape of linguistic systems by looking at recursive numeral systems. Previous work has argued that these systems optimise the trade-off between lexicon size and average morphosyntatic complexity (Denić and Szymanik, 2024). However, showing that only natural-language-like systems optimise this trade-off has proven elusive, and existing solutions rely on ad-hoc constraints to rule out unnatural systems (Yang and Regier, 2025). Drawing on the Minimum Description Length (MDL) approach, we argue that recursive numeral systems are better viewed as efficient with regard to their regularity and processing complexity. We show that our MDL-based measures of regularity and processing complexity better capture the key differences between attested, natural systems and theoretically possible ones, including “optimal” recursive numeral systems from previous work, and that the ad-hoc constraints naturally follow from regularity. Our approach highlights the need to incorporate regularity across sets of forms in studies attempting to measure efficiency in language.

Recursive numeral systems are highly regular and easy to process

Existing challenges in misinformation exposure and susceptibility vary across demographics, as some populations are more vulnerable to misinformation than others. Large language models (LLMs) introduce new dimensions to these challenges through their ability to generate persuasive content at scale and reinforcing existing biases. This study investigates the bidirectional persuasion dynamics between LLMs and humans when exposed to misinformative content. We analyze human-to-LLM influence using human-stance datasets and assess LLM-to-human influence by generating LLM-based persuasive arguments. Additionally, we use a multi-agent LLM framework to analyze the spread of misinformation under persuasion among demographic-oriented LLM agents. Our findings show that demographic factors influence LLM susceptibility, with up to 15 percentage point differences in correctness across groups. Multi-agent LLMs also exhibit echo chamber behavior, aligning with human-like group polarization patterns. Therefore, this work highlights demographic divides in misinformation dynamics and offers insights for future interventions.

Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions

Large language models (LLMs) are known to "hallucinate" by generating false or misleading outputs. Hallucinations pose various harms, from erosion of trust to widespread misinformation. Existing hallucination evaluation, however, focuses only on correctness and often overlooks consistency, necessary to distinguish and address these harms. To bridge this gap, we introduce prompt multiplicity, a framework for quantifying consistency in LLM evaluations. Our analysis reveals significant multiplicity (over 50% inconsistency in benchmarks like Med-HALT), suggesting that hallucination-related harms have been severely misunderstood. Furthermore, we study the role of consistency in hallucination detection and mitigation. We find that: (a) detection techniques detect consistency, not correctness, and (b) mitigation techniques like RAG, while beneficial, can introduce additional inconsistencies. By integrating prompt multiplicity into hallucination evaluation, we provide an improved framework of potential harms and uncover critical limitations in current detection and mitigation strategies.

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