Morocco

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.

EACL 2026

What Does Infect Mean to Cardio? Investigating the Role of Clinical Specialty Data in Medical LLMs

clincial specialty dataset

medical qa

clinical nlp

Welcome to the largest national, multi-specialty research event **featuring research from 750 medical students, residents and fellows, and international medical graduates.**


#### Event Details
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Charles Lopresto, DO

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AMA Research Challenge 2022

Welcome to the largest national, multi-specialty research event featuring research from 750 medical students, residents and fellows, and international medical graduates.


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)

This paper introduces DeepPavlov 1.1, a new version of an open-source library for natural language processing (NLP). DeepPavlov 1.1 supports both traditional NLP tasks (like named entity recognition, sentiment classification) and new tasks needed to enhance LLMs truthfulness and reliability. These tools include: a hallucination detection model, an evergreen question classifier, and a toxicity classifier. The library is easy to use, flexible, and works with many languages. It is designed to help researchers and developers build better, safer AI systems that use language. It is publicly available under the Apache 2.0 license and includes access to an interactive online demo.

DeepPavlov Strikes Back: A Toolkit for Improving LLM Reliability and Trustworthiness

Prompt optimization has become crucial for enhancing the performance of large language models (LLMs) across a broad range of tasks. Although many research papers show its effectiveness, practical adoption is hindered as existing implementations are often tied to unmaintained and isolated research codebases. To address this, we introduce promptolution, a unified and modular open-source framework that provides all components required for prompt optimization within a single extensible system for both practitioners and researchers. It integrates multiple contemporary discrete prompt optimizers while remaining agnostic to the underlying LLM implementation.

promptolution: A Unified, Modular Framework for Prompt Optimization

We introduce T-pro 2.0, an open-weight Russian LLM for hybrid reasoning and efficient inference.
The model supports direct answering and reasoning-trace generation, using a Cyrillic-dense tokenizer and an adapted EAGLE speculative-decoding pipeline to reduce latency. To enable reproducible and extensible research, we release the model weights, the T-Wix 500k instruction corpus, the T-Math reasoning benchmark, and the EAGLE weights on HuggingFace. These resources allow users to study Russian-language reasoning and to extend or adapt both the model and the inference pipeline. A public web demo exposes reasoning and non-reasoning modes and illustrates the speedups achieved by our inference stack across domains.
T-pro 2.0 thus serves as an accessible open system for building and evaluating efficient, practical Russian LLM applications.


Demo: https://t-pro2eagle.streamlit.app/


https://huggingface.co/collections/t-tech/t-pro-20

T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground

We present SDialog, an MIT-licensed open-source Python toolkit that unifies dialog generation, evaluation and mechanistic interpretability into a single end-to-end framework for building and analyzing LLM-based conversational agents. Built around a standardized dialog representation, SDialog provides: (1) persona-driven multi-agent simulation with composable orchestration for controlled synthetic dialog generation, (2) comprehensive evaluation combining linguistic metrics, LLM-as-a-judge and functional correctness validators, (3) mechanistic interpretability tools for activation inspection and steering via feature ablation and induction, and (4) audio generation with full acoustic simulation including 3D room modeling and microphone effects. The toolkit integrates with all major LLM backends, enabling mixed-backend experiments under a unified API.
By coupling generation, evaluation, and interpretability in a dialog-centric architecture, SDialog enables researchers to build, benchmark and understand conversational systems more systematically.

SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation

Large language models (LLMs) have expanded the potential for AI-assisted scientific claim verification, yet existing systems often exhibit unverifiable attributions, shallow evidence mapping, and hallucinated citations. We present SciTrue, a claim verification system providing source-level accountability and evidence traceability. SciTrue links each claim component to explicit, verifiable scientific sources, enabling users to inspect and challenge model inferences, addressing limitations of both general-purpose and search-augmented LLMs. In a human evaluation of 300 attributions, SciTrue achieves high fidelity in summary traceability, attribution accuracy, and context alignment, substantially outperforming RAG-based baselines such as GPT-4o-search-preview and Perplexity Sonar Pro. These results underscore the importance of principled attribution and context-aware reasoning in AI-assisted scientific verification. A system demo is available at \href{https://www.scitrue.org/platform/?access=EACL2026DEMO}{www.scitrue.org}.

SciTrue: Evidence-Grounded Claim Verification in Science

Large language model (LLM) based search agents iteratively generate queries, retrieve external information, and reason to answer open-domain questions. While researchers have primarily focused on improving their utility, their safety behaviors remain underexplored. In this paper, we first evaluate search agents using red-teaming datasets and find that they are more likely to produce harmful outputs than base LLMs. For example, when asked ``How can I track someone's location without their consent?'', a base model refuses, whereas a search agent designed to retrieve and cite sources may lower its refusal threshold, fetch documents (e.g., court cases), and, once appended, synthesize them into an informative yet unsafe summary. We further show that utility-oriented fine-tuning intensifies this risk, motivating joint alignment of safety and utility. We present SafeSearch, a multi-objective reinforcement learning approach that couples a final-output safety/utility reward with a novel query-level shaping term that penalizes unsafe queries and rewards safe ones. Experiments show that SafeSearch reduces agent harmfulness by over 80% across three red-teaming datasets while producing safe, helpful responses, and matches the QA performance of a utility-only finetuned agent; further analyses confirm the effectiveness of the query-level reward in jointly improving safety and utility.

SafeSearch: Do Not Trade Safety for Utility in LLM Search Agents

Plane geometry problem solving (PGPS) has recently gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. Despite the growing interest in PGPS, the research community still lacks a comprehensive overview that systematically synthesizes recent work in PGPS. To fill this gap, we present a survey of existing PGPS studies. We first categorize PGPS methods into an encoder-decoder framework and summarize the corresponding output formats used by their encoders and decoders. Subsequently, we classify and analyze these encoders and decoders according to their architectural designs. Finally, we outline major challenges and promising directions for future research. In particular, we discuss the hallucination issues arising during the encoding phase within encoder-decoder architectures, as well as the problem of data leakage in current PGPS benchmarks.

Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey

Recent multimodal large language models (MLLMs) extend language understanding beyond text to speech, enabling unified reasoning across modalities. While biases in text-based LLMs have been widely examined, their persistence and manifestation in spoken inputs remain underexplored. This work presents the first systematic investigation of speech bias in multilingual MLLMs. We construct and release the BiasInEar Dataset, a speech-augmented benchmark based on Global MMLU Lite, spanning English, Chinese, and Korean, balanced by gender and accent, and totaling 70.8 hours (4,249 minutes) of speech with 11,200 questions. Using four complementary metrics (accuracy, entropy, APES, and Fleiss’), we evaluate nine representative models under linguistic language and accent, demographic gender, and structural option order perturbations. Our findings reveal that MLLMs are relatively robust to demographic factors but highly sensitive to language and option order, suggesting that speech can amplify existing structural biases. Moreover, architectural design and reasoning strategy substantially affect robustness across languages. Overall, this study establishes a unified framework for assessing fairness and robustness in speech-integrated LLMs, bridging the gap between text- and speech-based evaluation.

Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations

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.

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