Morocco

Retrieval-Augmented Generation (RAG) systems depend critically on retrieval quality to enable accurate, contextually relevant LLM responses. While LLMs excel at synthesis, their RAG performance is bottlenecked by document relevance. We evaluate advanced retrieval techniques including embedding model comparison, Reciprocal Rank Fusion (RRF), embedding concatenation and list-wise and adaptive LLM-based re-ranking, demonstrating that zero-shot LLMs outperform traditional cross-encoders in identifying high-relevance passages. We also explore context-aware embeddings, diverse chunking strategies, and model fine-tuning. All methods are rigorously evaluated on a proprietary dataset powering our deployed production chatbot, with validation on three public benchmarks: FiQA, HotpotQA, and SciDocs. Results show consistent gains in Recall@10, closing the gap with Recall@50 and yielding actionable pipeline recommendations. By prioritizing retrieval enhancements, we significantly elevate downstream LLM response quality in real-world, customer-facing applications.

EACL 2026

Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot

llm re-ranking

reciprocal rank fusion (rrf)

ranking

retrieval

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. 

Generative Retrieval (GR) maps queries to documents by generating discrete identifiers (DocIDs). However, offline DocID assignment and constrained decoding often prevent GR from capturing query-specific intent, especially when documents express multiple or unseen intents (i.e., intent misalignment). We introduce Dynamic Docid Decoding (D3), an inference-time mechanism that adaptively refines DocIDs through delayed, query-informed identifier expansion. D3 uses (a) verification to detect intent misalignment and (b) dynamic decoding to extend DocIDs with query-aligned tokens, even those absent from the pre-indexed vocabulary, enabling plug-and-play DocID expansion beyond the static vocabulary while adding minimal overhead. Experiments on NQ320k and MS-MARCO show that D3 consistently improves retrieval accuracy, especially on unseen and multi-intent documents, across various GR models, including a +2.4\%p nDCG@10 gain on the state-of-the-art model.

D3: Dynamic Docid Decoding for Multi-Intent Generative Retrieval

Arabic calligraphy represents one of the richest visual traditions of the Arabic language, blending linguistic meaning with artistic form. Although multimodal models have advanced across languages, their ability to process Arabic script, especially in artistic and stylized calligraphic forms, remains largely unexplored. To address this gap, we present DuwatBench, a benchmark of 1,050 curated samples containing about 1,400 words across 6 classical and modern calligraphic styles, each paired with sentence-level detection annotations. The dataset reflects real-world challenges in Arabic writing, such as complex stroke patterns, dense ligatures, and stylistic variations that often challenge standard text recognition systems. Using DuwatBench, we evaluated 13 leading Arabic and multilingual multimodal models and show that while they perform well in clean text, they struggle with calligraphic variation, artistic distortions, and precise visualвЂ“text alignment. By publicly releasing DuwatBench and its annotations, we aim to advance culturally grounded multimodal research, foster fair inclusion of Arabic language and visual heritage in AI systems, and support continued progress in this area. Our benchmark and the evaluation suit will be publicly available.

DuwatBench: Bridging Language and Visual Heritage through an Arabic Calligraphy Benchmark for Multimodal Understanding

For socially sensitive tasks like hate speech detection, the quality of explanations from Large Language Models (LLMs) is crucial for factors like user trust and model alignment. While Persona prompting (PP) is increasingly used as a way to steer model towards user-specific generation, its effect on model rationales remains underexplored. We investigate how LLM-generated rationales vary when conditioned on different simulated demographic personas. Using datasets annotated with word-level rationales, we measure agreement with human annotations from different demographic groups, and assess the impact of PP on model bias and human alignment. Our evaluation across three LLMs results reveals three key findings: (1) PP improving classification on the most subjective task (hate speech) but degrading rationale quality. (2) Simulated personas fail to align with their real-world demographic counterparts, and high inter-persona agreement shows models are resistant to significant steering. (3) Models exhibit consistent demographic biases and a strong tendency to over-flag content as harmful, regardless of PP. Our findings reveal a critical trade-off: while PP can improve classification in socially-sensitive tasks, it often comes at the cost of rationale quality and fails to mitigate underlying biases, urging caution in its application.

Persona Prompting as a Lens on LLM Social Reasoning

Medical reasoning in large language models seeks to replicate clinicians' cognitive processes in interpreting patient data and making diagnostic decisions. However, widely used benchmarksвЂ”such as MedQA, MedMCQA, and PubMedQAвЂ”mix questions that require multi-step reasoning with those answerable through factual recall, complicating evaluation. We demonstrate this by training a PubMedBERT-based classifier on expert-curated labels and applying it to 11 widely used biomedical QA benchmarks, where we find that only 32.8% of the questions require multi-step reasoning, indicating that current evaluations largely measure recall. This stratified evaluation of biomedical models (HuatuoGPT-o1, MedReason, m1) and general-domain models (DeepSeek-R1, o4-mini, Qwen3) reveals consistently lower performance on reasoning versus knowledge (e.g., HuatuoGPT-o1: 56.9% vs. 44.8%). Beyond accuracy, we assess robustness through adversarial evaluations in which models are prefixed with uncertainty-inducing statements; biomedical reasoning models degrade sharply in this setting (e.g., MedReason: 50.4% в†’ 24.4%), with declines especially pronounced on reasoning-heavy questions. Finally, we show that fine-tuning on high-quality reasoning examples augmented with adversarial traces, followed by reinforcement learning with GRPO, improves both robustness and accuracy across knowledge and reasoning subsets.

Reasoning or Knowledge: Stratified Evaluation of Biomedical LLMs

Despite their impressive performance, vision-language models (VLMs) still struggle on culturally situated inputs. To understand how VLMs process culturally grounded information, we study the presence of culture-sensitive neurons, i.e. neurons whose activations show preferential sensitivity to inputs associated with particular cultural contexts. We examine whether such neurons are important for culturally diverse visual question answering and where they are located. Using the CVQA benchmark, we identify neurons of culture selectivity and perform causal tests by deactivating the neurons flagged by different identification methods. Experiments on three VLMs across 25 cultural groups demonstrate the existence of neurons whose ablation disproportionately harms performance on questions about the corresponding cultures, while having minimal effects on others. Moreover, we propose a new margin-based selector: Contrastive Activation Selection (CAS), and show that it outperforms existing probability- and entropy-based methods in identifying culture-sensitive neurons. Finally, our layer-wise analyses reveals that such neurons tend to cluster in certain decoder layers. Overall, our findings shed new light on the internal organization of multimodal representations.

Finding Culture-Sensitive Neurons in Vision-Language Models

Multiple-Choice Questions (MCQs) are often used to assess knowledge, reasoning abilities, and even values encoded in large language models (LLMs). While the effect multilingualism has been studied on LLM factual recall, few studies have taken interest in language-induced variation in value-laden MCQ responses. This paper seeks to bridge that gap: do multilingual LLMs behave like polyglots, i.e. answer the same question the same way across languages, or do they answer value-laden MCQs depending on the language, like a multitude of monolingual models? Unlike prior work relying on machine translation or ad hoc prompts, we release a new corpus solely comprising human-translated survey questions aligned in 8 European languages. We administer a subset of those questions to about forty multilingual LLMs of various sizes, manufacturers and alignment-fine-tuning status under comprehensive, controlled prompt variations including answer order, symbol type, and tail character. Our results show that while instruction-tuned, larger models display higher consistency overall, that consistency varies greatly across questions, with certain MCQs eliciting total agreement within and across models while other leave LLM answers evenly split. Language-specific behavior seems to arise in all of the most consistent models, but only on certain questions, warranting a further study of the selective effect of preference fine-tuning.

Polyglots or Multitudes? Multilingual LLM Answers to Value-laden Multiple-Choice Questions

ICD coding is the process of mapping unstructured text from Electronic Health Records (EHRs) to standardised codes defined by the International Classification of Diseases (ICD) system. In order to promote trust and transparency, existing explorations on the explainability of ICD coding models primarily rely on attention-based rationales and qualitative assessments conducted by physicians, yet lack a systematic evaluation across diverse types of rationales using consistent criteria and high-quality rationale-annotated datasets specifically designed for the ICD coding task. Moreover, there remains a scarcity of dedicated methods explicitly trained to generate plausible rationales. In this work, we present evaluations of the explainability of rationales in ICD coding, focusing on two fundamental dimensions: faithfulness and plausibility. To enable the systematic evaluation of plausibility, we construct a new rationale-annotated dataset specifically for ICD coding, constructed in accordance with the updating MIMIC-IV database and the ICD-10 coding system. This dataset features richer annotations with diverse levels of granularity. We conduct a comprehensive evaluation across three types of ICD coding rationales: entity-level mentions derived from entity linking dataset, LLM-generated rationales, and attention-based rationales. Building upon the strong plausibility exhibited by LLM-generated rationales, we further leverage them as distant supervision signals to develop rationale learning methods. Additionally, by prompting the LLM with few-shot human-annotated examples from our dataset, we achieve notable improvements in the plausibility of rationale generation in both the teacher LLM and the student rationale learning models.

Evaluation and LLM-Guided Learning of ICD Coding Rationales

The potential misuse and misalignment of language models (LMs) is a central safety concern. This work presents Self-Destruct, a novel mechanism to restrict specific behaviors in LMs by leveraging overlooked properties of the underlying hardware. We observe that the LM frameworks use limited-precision formats (e.g., FP32), which are vulnerable to overflow errors during matrix multiplications. Exploiting this property, Self-Destruct replaces selected weights in pre-trained LM layers with values that act as traps, triggering a system error only when the model engages in targeted behaviors, such as harmful text generation, while leaving normal functionality unaffected. Unlike post-hoc filters, this safeguard is embedded directly within the model, introduces neither inference overhead nor auxiliary models, and requires only a set of examples for calibration. Experiments on Llama-3.2 and Qwen-2.5 demonstrate that Self-Destruct provides competitive protection against jailbreak attacks while preserving accuracy on standard benchmarks. Our results highlight the potential of hardware-aware safeguards as an efficient, low-overhead complement to existing LM defenses.

Safeguarding Language Models via Self-Destruct Trapdoor

Quantifying uncertainty in large language models (LLMs) is crucial for applications where safety is a concern, as it helps identify factually incorrect LLM answers, commonly referred to as hallucinations. Recently, advancements have been made in quantifying uncertainty, specifically by incorporating the semantics of sampled answers to estimate entropy. These methods typically rely on a normalized probability that is calculated using a limited number of sampled answers. However, we note these estimation methods fail to account for the effects of the semantics that are possible to be obtained as answers, but are not observed in the sample. This is a significant oversight, since a heavier tail of unobserved answer probabilities indicates a higher level of overall uncertainty. To alleviate this issue, we propose Evidential Semantic Entropy (EVSE), which leverages evidence theory to represent both total ignorance arising from unobserved answers and partial ignorance stemming from the semantic relationships among the observed answers. Experiments show that EVSE significantly improves uncertainty quantification performance. Our code is available at: \url{https://anonymous.4open.science/r/EvidentialSemanticEntropy-6371}.

Evidential Semantic Entropy for LLM Uncertainty Quantification

Content moderation filters are a critical safeguard against alignment failures in language models. Yet most existing filters focus narrowly on general safety and overlook cultural context. In this work, we introduce {AnonGuard} (Masked for anonymity), a bilingual moderation filter that evaluates both safety and cultural alignment in Arabic and English. We construct a dataset of over 468K prompt and response pairs, drawn from synthetic and public datasets, scored by a panel of LLM judges on harmlessness and cultural awareness, and use it to train two filter variants. To rigorously evaluate cultural alignment, we further develop the first benchmark targeting Arabic cultural contexts, comprising 1008 norm-sensitive prompts with LLM-generated responses annotated by human raters. Results show that {AnonGuard} achieves stronger agreement with human annotations than inter-annotator reliability, while matching the performance of state-of-the-art filters on safety benchmarks. These findings highlight the importance of integrating cultural awareness into moderation and establish {AnonGuard} as a practical step toward more context-sensitive safeguards.

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