Morocco

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.

EACL 2026

D3: Dynamic Docid Decoding for Multi-Intent Generative Retrieval

dynamic decoding

generative retrieval

information retrieval

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



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. 

In this work, we present a modular and interpretable framework that uses Large Language Models (LLMs) to automate candidate assessment in recruitment. The system integrates diverse sources—including job descriptions, CVs, interview transcripts, and HR feedback—to generate structured evaluation reports that mirror expert judgment. Unlike traditional ATS tools that rely on keyword matching or shallow scoring, our approach employs role-specific, LLM-generated rubrics and a multi-agent architecture to perform fine-grained, criteria-driven evaluations. The framework outputs detailed assessment reports, candidate comparisons, and ranked recommendations that are transparent, auditable, and suitable for real-world hiring workflows. 


Beyond rubric-based analysis, we introduce an LLM-Driven Active Listwise Tournament mechanism for candidate ranking. Instead of noisy pairwise comparisons or inconsistent independent scoring, the LLM ranks small candidate subsets (“mini-tournaments”), and these listwise permutations are aggregated using a Plackett–Luce model. An active-learning loop selects the most informative subsets, producing globally coherent and sample-efficient rankings. This adaptation of listwise LLM preference modeling—previously explored in financial asset ranking —provides a principled and highly interpretable methodology for large-scale candidate ranking in talent acquisition.

Agentic AI for Human Resources: LLM-Driven Candidate Assessment (presented virtually)

Aircraft Maintenance Technicians (AMTs) spend up to 30\% of work time searching manuals—a documented efficiency bottleneck in MRO operations where every procedure must be traceable to certified sources. We present a compliance-preserving retrieval system that adapts LLM reranking and semantic search to aviation MRO environments by operating alongside, rather than replacing, certified legacy viewers. The system constructs revision-robust embeddings from ATA chapter hierarchies and uses vision-language parsing to structure certified content, allowing technicians to preview ranked tasks and access verified procedures in existing viewers. Evaluation on 49k synthetic queries achieves >90\% retrieval accuracy, while bilingual controlled studies with 10 licensed AMTs demonstrate 90.9\% top-10 success rate and 95\% reduction in lookup time—from 6-15 minutes to 18 seconds per task. These gains provide concrete evidence that semantic retrieval can operate within strict regulatory constraints and meaningfully reduce operational workload in real-world multilingual MRO workflows.

A Compliance-Preserving Retrieval System for Aircraft MRO Task Search

Conversational Agents have become ubiquitous across application domains, such as, shopping assistants, medical diagnosis, autonomous task planning etc. Users interacting with these agents often fail to understand how to start a conversation or what to ask next to obtain the desired information. To enable seamless and hassle-free user-agent interactions, we introduce Next Question Suggestions (NQS), which are essentially highly relevant follow-up question recommendations that act as conversation starters or discover-ability tools to capture non-trivial user intents, leading to more engaging conversations. Relying on LLMs for both response as well as NQS generation is a costly ask in latency-constrained commercial settings, with an added risk of handling potentially unsafe or unanswerable generated queries. A key component of building an efficient low-latency NQS experience is, therefore, retrieval (or embedding) models that fetch the most-relevant candidate questions from an offline pre-curated Question Bank (QB). Off-the-shelf embedding models cannot capture domain-specific nuances and more importantly the directionality inherent in follow-up question recommendations. In this work, we propose an end-to-end retrieval system, DIRECT that is optimized to model directional relevance. Given a user query, it produces a ranked list of highly relevant follow-up question recommendations within 1 sec. Our system also contains an LLM-as-a-judge component, tuned on proprietary user-agent interaction logs, to evaluate the end-to-end performance in terms of CTR.

DIRECT: Directional Relevance in Conversational Trajectories

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

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

Grammar comprehension is a critical capability for large language models (LLMs) to achieve fluency in a target language. In low-resource settings, such as Arabic, limited availability of high-quality data can lead to significant gaps in grammatical understanding, making systematic evaluation essential. We introduce Nahw, a comprehensive benchmark for Arabic grammar that covers both theoretical knowledge and practical applications, including grammatical error detection, correction, and explanation. We evaluate a range of LLMs on these tasks and find that many models still exhibit substantial deficiencies in Arabic grammar comprehension with GPT-4o achieving a score of 67%, ALLaM-7B achieving 42%, and Fanar-1-9B achieving 35% on average over all tasks. Our experiments also demonstrate that while fine-tuning with synthetic data can improve performance, it does not match the effectiveness of training on natural, high-quality data.

Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation

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.

FanarGuard: A Culturally-Aware Moderation Filter for Arabic Language Models

In this paper, we introduce S-MedQA, an English medical question-answering (QA) dataset for benchmarking large language models (LLMs) in fine-grained clinical specialties. S-MedQA has over 20k examples, covers 15 medical specialties, and QA pairs can have multiple specialty annotations (e.g., when a question is cross-disciplinary), constructed with both machine and expert verification to maximize data availability. We use S-MedQA to investigate the role of clinical specialty data in the knowledge-intensive scenario of medical QA. Our results show that 1) training on data from a clinical specialty does not necessarily lead to best performance on that specialty, and 2) regardless of the specialty the LLM was fine-tuned on, token probabilities of clinically relevant terms increase consistently across all specialties. Thus, we hypothesize improvement gains are derived mostly from domain shifting (e.g., general to medical) rather than specialty-specific knowledge injection, and suggest rethinking the role of fine-tuning data in the medical domain. To motivate further advancements in the clinical NLP field, we release S-MedQA and all code needed to reproduce all our experiments to the research community.

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