Morocco

Through the course of hospital treatment, a large number of electronic health records (EHRs) are created for a patient, detailing aspects of care history such as lab results, physician notes, and treatments administered. At the conclusion of treatment, this collection of EHRs must be summarized into a discharge summary, describing the course or care clearly and cohesively. In this paper, we present the design and development of a clinical summarization system integrated into a live German hospital workflow to help with the generation of discharge summaries. We first describe the system, its components, and its context of use within a hospital, before performing a number of experiments to gain insights into how best to use and evaluate our system. We investigate summarization performance across multiple input encoding strategies, compare expert judgments against automatic evaluation of summaries, and analyze the consistency of model summaries across multiple text generations. This work not only acts as a case study to demonstrate the feasibility of LLM integration into healthcare infrastructure, but also provides actionable insights into the use and evaluation of such systems.

EACL 2026

Medical Summarization in Practice: Design, Deployment, and Analysis of a Clinical Summarization System for a German Hospital

summarization system development

encoding strategy

medical summarization

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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. 

Speech large language models (LLMs) observe paralinguistic cues such as prosody, emotion, and non-verbal sounds—crucial for intent understanding. However, leveraging these cues faces challenges: limited training data, annotation difficulty, and models exploiting lexical shortcuts over paralinguistic signals. We propose multi-task reinforcement learning (RL) with chain-of-thought prompting that elicits explicit affective reasoning. To address data scarcity, we introduce a paralinguistics-aware speech LLM (PALLM) that jointly optimizes sentiment classification from audio and paralinguistics-aware response generation via a two-stage pipeline. Experiments demonstrate that our approach improves paralinguistics understanding over both supervised baselines and strong proprietary models (Gemini-2.5-Pro, GPT-4o-audio), by 8-12% on Expresso, IEMOCAP, and RAVDESS. The results show that modeling paralinguistic reasoning with multi-task RL is crucial for building emotionally intelligent speech LLMs.

Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning

Safety alignment of large language models (LLMs) is mostly evaluated in English and contract-bound, leaving multilingual vulnerabilities understudied. We introduce Indic Jailbreak Robustness (IJR) a judge-free benchmark for adversarial safety across 12 Indic and South Asian languages (~2.09B speakers), covering 45,216 prompts in JSON (contract-bound) and Free (naturalistic) tracks. IJR reveals three patterns. (1) Contracts inflate refusals but do not stop jailbreaks: in JSON, LLaMA and Sarvam exceed $0.92$ JSR, and in Free all models reach ~1.0 with refusals collapsing. (2) English→Indic attacks transfer strongly, with format wrappers often outperforming instruction wrappers. (3) Orthography matters: romanized/mixed inputs reduce JSR under JSON, with correlations to romanization share and tokenization ρ ≈ 0.28–0.32 indicating systematic effects. Human audits confirm detector reliability, and lite-to-full comparisons preserve conclusions. IJR offers a reproducible multilingual stress test revealing risks hidden by English-only, contract-focused evaluations, especially for South Asian users who frequently code-switch and romanize.

IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages

Agentic AI workflow converts noisy pharmaceutical batch records into validated JSON using hybrid OCR, vision–language and schema-guided LLMs, cutting QA review from hours to minutes while preserving GMP-critical structure.

From Paper to Structured JSON: An Agentic AI Workflow for Compliant BMR Digital Transformation

Digital footprints—records of individuals’ interactions with digital systems—are essential for studying behavior, developing personalized applications, and training machine learning models. However, research in this area is often hindered by the scarcity of diverse and accessible data. To address this limitation, we propose a novel method for synthesizing realistic digital footprints using large language model (LLM) agents. Starting from a structured user profile, our approach generates diverse and plausible sequences of user events, ultimately producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc. Intrinsic evaluation results demonstrate that the generated dataset is more diverse and realistic than existing baselines. Moreover, models fine-tuned on our synthetic data outperform those trained on other synthetic datasets when evaluated on real-world out-of-distribution tasks.

PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents

Automatic Speech Recognition (ASR) plays an important role in healthcare but faces unique challenges. Medical audio often contains specialized terminology, such as medication names, which existing ASR systems struggle to transcribe accurately. High error rates arise from pronunciation variability, the continual introduction of new terms, and the scarcity of high-quality labeled data—whose collection is costly and requires medical expertise. Although synthetic datasets partially alleviate this problem, they fail to capture the noise and variability of real-world recordings. Moreover, ASR models trained in controlled environments are highly sensitive to noise, leading to degraded performance in clinical settings. To address these limitations, we propose an unsupervised continual learning ASR framework that adapts to new data while preserving prior knowledge. This enables efficient domain adaptation without extensive retraining. Experiments on real-world medical audio demonstrate significant improvements over state-of-the-art baselines.

No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR

Patent drafting is complex due to its need for detailed technical descriptions, legal compliance, and visual elements. Although Large Vision-Language Models (LVLMs) show promise across various tasks, their application in automating patent writing remains underexplored. In this paper, we present PatentVision, a multimodal framework that integrates textual and visual inputs—such as patent claims and drawings—to generate complete patent specifications. Built on advanced LVLMs, PatentVision enhances accuracy by combining fine-tuned vision-language models with domain-specific training tailored to patents. Experiments reveal it surpasses text-only methods, producing outputs with greater fidelity and alignment with human-written standards. Its incorporation of visual data allows it to better represent intricate design features and functional connections, leading to richer and more precise results. This study underscores the value of multimodal techniques in patent automation, providing a scalable tool to reduce manual workloads and improve consistency. PatentVision not only advances patent drafting but also lays groundwork for broader use of LVLMs in specialized areas, potentially transforming intellectual property management and innovation processes.

PatentVision: A multimodal method for drafting patent applications

Multimodal Large Language Models (MLLMs) struggle with Long Video Understanding (LVU) due to their limited context window and the distributed nature of salient information across many redundant frames. To address this, we present VideoMind, a novel training free framework for LVU designed to mimic a human reasoning process. The framework is orchestrated by an MLLM that breaks down a user's query into a series of simpler, actionable sub-queries. For each sub query, the MLLM reconfigures itself by invoking specialized `modes' that are instantiations of the same MLLM, but with appropriately tailored context for the given sub query to extract targeted evidence. After gathering this evidence, the model resumes its role as the orchestrator which evaluates the results and decides if an answer is complete or if it must refine its strategy by engaging further modes with new context. Our specialized operational modes include: 1) a Multi-Scale Temporal Search mode to identify and summarize relevant video sub-snippets at varying time scales, and 2) a Single-Frame Visual Detail mode for precise spatial localization of objects. This dynamic allocation of computation yields state-of-the-art results on the Video-MME, LongVideo, and MLVU benchmarks, achieving 77.6% performance on Video MME using Qwen 2.5 72B (4.8% enhancement) while also yielding a 5% improvement on Llama 4 Scout.

VideoMind: Thinking in Steps for Long Video Understanding

Clinical Question-Answering (CQA) industry systems are increasingly rely on Large Language Models (LLMs), yet their deployment is often guided by the assumption that domain-specific fine-tuning is essential. Although specialised medical LLMs such as BioBERT, BioGPT, and PubMedBERT remain popular, they face practical limitations including narrow coverage, high retraining costs, and limited adaptability. Efforts based on Supervised Fine-Tuning (SFT) have attempted to address these assumptions but continue to reinforce what we term the SPECIALISATION FALLACY—the belief that specialised medical LLMs are inherently superior for CQA. To address this assumption, we introduce MEDASSESS-X, a deployment-industry-oriented CQA framework that applies alignment at inference time rather than through SFT. MEDASSESS-X uses lightweight steering vectors to guide model activations toward medically consistent reasoning without updating model weights or requiring domain-specific retraining. This inference-time alignment layer stabilises CQA performance across both general-purpose and specialised medical LLMs, thereby resolving the SPECIALISATION FALLACY. Empirically, MEDASSESS-X delivers consistent gains across all LLM families, improving Accuracy by up to +6%, Factual Consistency by +7%, and reducing Safety Error Rate by as much as 50%.

Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning?

Long-context Large Language Models (LLMs) face significant memory bottlenecks during inference due to the linear growth of key-value (KV) cache with sequence length. While individual optimization techniques like KV cache quantization, chunked prefill, and model weight quantization have shown promise, their joint effects and optimal configurations for edge deployment remain underexplored. We introduce KV Pareto, a systems-level framework that systematically maps the trade-off frontier between total memory consumption and task accuracy across these three complementary optimization techniques. Our framework evaluates multiple LLM architectures (Qwen, Llama, Mistral) with varying KV quantization schemes (int2/4/8, mixed-precision), granularities (per-token, per-tensor, per-block), and 4-bit weight quantization via AWQ. Our framework identifies model-specific Pareto-optimal configurations that achieve 68-78\% total memory reduction with minimal (1-3\%) accuracy degradation on long-context tasks. We additionally verify the selected frontiers on additional benchmarks of Needle-in-a-Haystack, GSM8k and MMLU as well as extended context lengths of up to 128k to demonstrate the practical need of joint optimization for efficient LLM inference.

KV Pareto: Systems-Level Optimization of KV Cache and Model Compression for Long Context Inference

We present MizanQA, a benchmark for assessing LLMs on Moroccan legal MCQs, many with multiple correct answers. Covering 1,776 expert-verified questions in Modern Standard Arabic enriched with Moroccan idioms, the dataset reflects influences from Maliki jurisprudence, customary law, and French legal traditions. Unlike single-answer settings, MizanQA features variable option counts, creating added difficulty. We evaluate multilingual and Arabic-centric models in zero-shot, native-Arabic prompts, measuring accuracy, a precision-penalized F1-like score, and calibration errors. Results show large performance gaps and miscalibration, particularly under stricter penalties. By scoping this benchmark to parametric knowledge only, we provide a baseline for future retrieval-augmented and rationale-focused setups.

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