Morocco

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 (&lt;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.

EACL 2026

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

real-time inference

layout-aware language model

medical prescriptions

lightweight transformer

entity extraction

language model

entity linking

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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.

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. 

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.

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

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

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

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

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.

Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicity

Machine translation (MT) systems universally degrade when faced with code-mixed text. This problem is more acute for low-resource languages that lack dedicated parallel corpora. This work directly addresses this gap for Vietnamese-English, a language context characterized by challenges including orthographic ambiguity and the frequent omission of diacritics in informal text. We introduce VietMix, the first expert-translated, naturally occurring parallel corpus of Vietnamese-English code-mixed text. We establish VietMix's utility by developing a data augmentation pipeline that leverages iterative fine-tuning and targeted filtering. Experiments show that models augmented with our data outperform strong back-translation baselines by up to +3.5 xCOMET points and improve zero-shot models by up to +11.9 points. Our work delivers a foundational resource for a challenging language pair and provides a validated, transferable framework for building and augmenting corpora in other low-resource settings.

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