Morocco

The increasing frequency of foodborne illnesses, safety hazards, and disease outbreaks in the food supply chain demands urgent attention to protect public health. These incidents, ranging from contamination to intentional adulteration of food and feed, pose serious risks to consumers, leading to poisoning, and disease outbreaks that lead to product recalls. Identifying and tracking the sources and pathways of contamination is essential for timely intervention and prevention. This paper explores the use of social media and regulatory news reports to detect food safety issues and disease outbreaks. We present an automated approach leveraging a multi-task sequence labeling and sequence classification model that uses a liquid time-constant neural network augmented with a graph convolution network to extract and analyze relevant information from social media posts and official reports. Our methodology includes the creation of annotated datasets of social media content and regulatory documents, enabling the model to identify foodborne infections and safety hazards in real-time. Preliminary results demonstrate that our model outperforms baseline models, including advanced large language models like LLAMA-3 and Mistral-7B, in terms of accuracy and efficiency. The integration of liquid neural networks significantly reduces computational and memory requirements, achieving superior performance with just 
bytes of memory, compared to the 20.3 GB of GPU memory needed by traditional transformer-based models. This approach offers a promising solution for leveraging social media data in monitoring and mitigating food safety risks and public health threats.

EACL 2026

A Graph-Augmented Liquid Neural Network for Extracting Food Hazards and Disease Outbreaks

liquid neural network

infection detection

food hazard

food recall

social media

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

workshop 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. 

Few-shot prompting with Large Language Models (LLMs) has emerged as a promising paradigm for advancing information extraction, particularly in data-scarce domains like biomedicine, where high annotation costs constrain the availability of training data. However, challenges persist in biomedical Named Entity Recognition (NER), where LLMs fail to achieve necessary accuracy and lag behind supervised fine-tuned models. In this study, we introduce FETA (\textit{First Extract, Tag Afterwards}), a two-stage approach for entity recognition that combines instruction-guided prompting and a novel self-verification strategy to improve accuracy and reliability of LLM predictions in domain-specific NER tasks. FETA achieves state-of-the-art results on multiple established biomedical datasets. Our experiments demonstrate that carefully designed prompts, using self-verification and instruction guidance, can steer general-purpose LLMs to outperform fine-tuned models in knowledge-intensive NER tasks, unlocking their potential for more reliable and accurate information extraction in resource-constrained settings.

Mind Your Steps in Biomedical Named Entity Recognition: First Extract, Tag Afterwards

Neuropsychiatric lupus (NPSLE) is characterized by inflammation in the brain with common symptoms of depression and anxiety. Early detection is crucial as it may change the treatment regimen; however, current approaches are costly and resource intensive. Therefore, we propose that leveraging current work using linguistics in NLP detection of mental health symptoms can be advantageous in early detection of NPSLE. This study is a proof-of-concept using 20 interviews from N=20 adolescents (10-17 years) diagnosed with Lupus. Our results suggest that linguistic feature-based models supported by Word2Vec embeddings offer an interpretable output compared with BERT models, while maintaining competitiveness in depression, and improvement over BERT in anxiety detection. This work may transform early screening methods in paediatric contexts and can be adapted to other clinical populations.

Linguistic Features Competitive with Bert! Leveraging Speech for Detection of Mental Health in Paediatric Lupus

We present DementiaBank-Emotion, the first multi-rater emotion annotation corpus for Alzheimer's disease (AD) speech. Annotating 1,492 utterances from 108 speakers for Ekman's six basic emotions and neutral, we find that AD patients express significantly more non-neutral emotions (16.9%) than healthy controls (5.7%; p < .001). Exploratory acoustic analysis suggests a possible dissociation: control speakers showed substantial F0 modulation for sadness (Delta = -3.45 semitones from baseline), whereas AD speakers showed minimal change (Delta = +0.11 semitones; interaction p = .023), though this finding is based on limited samples (sadness: n=5 control, n=15 AD) and requires replication. Within AD speech, loudness differentiates emotion categories, indicating partially preserved emotion-prosody mappings. We release the corpus, annotation guidelines, and calibration workshop materials to support research on emotion recognition in clinical populations.

DementiaBank-Emotion: A Multi-Rater Emotion Annotation Corpus for Alzheimer's Disease Speech (Version 1.0)

This paper presents an LLM-driven approach for constructing diverse social media datasets to measure and compare loneliness in the caregiver and non-caregiver populations. We introduce an expert-developed loneliness evaluation framework and an expert-informed typology for categorizing causes of loneliness for analyzing social media text. Using a human-validated data processing pipeline, we apply GPT-4o, GPT-5-nano, and GPT-5 to build a high-quality Reddit corpus and analyze loneliness across both populations. The loneliness evaluation framework achieved average accuracies of 76.09\% and 79.78\% for caregivers and non-caregivers, respectively. The cause categorization framework achieved micro-aggregate F1 scores of 0.825 and 0.80 for caregivers and non-caregivers, respectively. Across populations, we observe substantial differences in the distribution of types of causes of loneliness. Caregivers’ loneliness were predominantly linked to caregiving roles, identity recognition, and feelings of abandonment, indicating distinct loneliness experiences between the two groups. Demographic extraction further demonstrates the viability of Reddit for building a diverse caregiver loneliness dataset. Overall, this work establishes an LLM-based pipeline for creating high quality social media datasets for studying loneliness and demonstrates its effectiveness in analyzing population-level differences in the manifestation of loneliness.

Why Are We Lonely? Leveraging LLMs to Measure and Understand Loneliness in Caregivers and Non-caregivers

Accurate normalization of health-related expressions to standardized biomedical concepts is crucial for both healthcare and biomedical research. However, traditional string-based matching methods are limited by lexical variations. In this study, we propose a neural embedding-based normalization framework that utilizes an embedding model trained on biomedical terminology, generating over 3.59 million embeddings corresponding to UMLS terms and Concept Unique Identifiers (CUIs). For clinical data, CUIs were retrieved via semantic matching, while Twitter phrases were first processed using a large language model (LLM) to generate preferred terms prior to embedding-based CUI retrieval. Our approach substantially outperforms exact string matching and MetaMap Lite. For clinical data (3,144 phrases), normalization accuracy improved from 0.679 (string match) and 0.574 (MetaMap Lite) to 0.858. For Twitter data (102 phrases), accuracy increased from 0.235 (string match) and 0.118 (MetaMap Lite) to a range of 0.882 (Gemini 2.5 Flash) to 0.980 (GPT-4o mini). These findings highlight both the effectiveness of embedding-based semantic retrieval and the ability of LLMs to generate preferred terms, enhancing robustness in health concept normalization across diverse text sources.

Normalizing Health Concepts with Biomedical Embedding and LLMs

Large language models (LLMs) often default to single-label classification in zero-shot multi-label tasks—a tendency we term "conservative default". While few-shot prompting mitigates this, it introduces "example bias". We evaluate zero-shot strategies to modulate this tendency using 1,441 healthcare feedback records and two LLMs. We compare instruction-based methods with structural constraints that modify the token generation sequence, specifically an Enum-First format requiring domain enumeration before selection. Results show that structural constraints substantially reduce single-label rates (Magistral: 96% → 19%; Qwen3: 54% → 0.0%), though the latter suggests potential over-correction compared to human baselines (16.7–41.3%). These findings indicate that while output structure is a potent modulator of classification behavior by shifting the decision point upstream, its effect magnitude is model-dependent, necessitating empirical calibration to prevent spurious associations.

Modulating Multi-Label Tendency in Zero-Shot LLM Coding: The Effect of Output Structure on CDSS Feedback Analysis

Large Language Models (LLMs) demonstrate impressive performance across various multimodal tasks. However, their effectiveness in cross-cultural contexts remains limited due to the predominantly Western-centric nature of existing data and models. Meanwhile, multi-agent models have shown strong capabilities in solving complex tasks. In this paper, we evaluate the performance of LLMs in a multi-agent interaction setting for the novel task of multicultural image generation. Our key contributions are: (1) We introduce MosAIG, a Multi-Agent framework that enhances multicultural Image Generation by leveraging LLMs with distinct cultural personas; (2) We provide a dataset of 9,000 multicultural images spanning five countries, three age groups, two genders, 25 historical landmarks, and five languages; and (3) We demonstrate that multi-agent interactions outperform simple, no-agent models across multiple evaluation metrics, offering valuable insights for future research.Our dataset and models are available at https://anonymous.4open.science/r/MosAIG.

Multi-Agent Multimodal Models for Multicultural Text to Image Generation

Long-form educational videos contain valuable mentorship insights, but extracting structured knowledge from hours of unscripted content remains challenging. We introduce MentorQA, a publicly available dataset and framework for mentorship-focused question answering from multilingual long-form videos. The dataset includes nearly 9,000 question–answer pairs collected from 120 mentorship videos across four languages (English, Hindi, Chinese, and Romanian) and six topics. We compare four QA-generation models and introduce mentorship-oriented evaluation metrics that go beyond factual correctness to assess learning value. Through comprehensive evaluation with nine LLM judges and twelve human annotators, we find that Multi-Agent pipelines consistently produce higher-quality mentorship-focused QA, particularly for complex topics and low-resource languages. We release the dataset and evaluation framework to support future research in multilingual educational and mentorship-focused AI at https://anonymous.4open.science/r/MentorQA/.

MentorQA: Multi-Agent Multilingual Question Answering for Long-Form Mentorship Content

In this paper, we study the capabilities of large language models (LLMs) to adapt a concert moderation to diverse expertise levels of listeners. Our proof-of-concept concert moderator is based on retrieval-augmented generation (RAG) and uses few-shot audience modelling to infer listener's expertise. We study the capabilities of the system to adapt to three different listener's expertise levels. Two open domain LLMs are compared: gpt-oss:20b and llama3. The recognised differences among the models suggest that they vary in how directly they reproduce versus paraphrase retrieved information while maintaining semantic alignment.

From Novice to Expert: Generating Audience-Dependent Concert Moderations with RAG-LLMs

The advancement of Machine learning (ML), Large Audio Language Models (LALMs), and autonomous AI agents in Music Information Retrieval (MIR) necessitates a shift from static tagging to rich, human-aligned representation learning. However, the scarcity of open-source infrastructure capable of capturing the subjective nuances of audio annotation remains a critical bottleneck. This paper introduces LabelBuddy, an open-source collaborative auto-tagging audio annotation tool designed to bridge the gap between human intent and machine understanding. Unlike static tools, it decouples the interface from inference via containerized backends, allowing users to plug in custom models for AI-assisted pre-annotation. We describe the system architecture, which supports multi-user consensus, containerized model isolation, and a roadmap for extending agents and LALMs. Code available at https://github.com/GiannisProkopiou/gsoc2022-Label-buddy.

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