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Multilingual Medical Reasoning for Question Answering with Large Language Models
Pietro Ferrazzi, Aitor Soroa, Rodrigo Agerri
Large Language Models (LLMs) with reasoning capabilities have recently demonstrated strong potential in medical Question Answering (QA). Existing approaches are largely English-foc…
BERnaT: Basque Encoders for Representing Natural Textual Diversity
Ekhi Azurmendi, Joseba Fernandez de Landa, Jaione Bengoetxea +5
Language models depend on massive text corpora that are often filtered for quality, a process that can unintentionally exclude non-standard linguistic varieties, reduce model robus…
Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque
Oscar Sainz, Naiara Perez, Julen Etxaniz +9
Instructing language models with user intent requires large instruction datasets, which are only available for a limited set of languages. In this paper, we explore alternatives to…
EuskañolDS: A Naturally Sourced Corpus for Basque-Spanish Code-Switching
Maite Heredia, Jeremy Barnes, Aitor Soroa
Code-switching (CS) remains a significant challenge in Natural Language Processing (NLP), mainly due a lack of relevant data. In the context of the contact between the Basque and S…
Conditioning LLMs to Generate Code-Switched Text
Maite Heredia, Gorka Labaka, Jeremy Barnes +1
Code-switching (CS) is still a critical challenge in Natural Language Processing (NLP), due to the limited availability of large-scale, diverse CS datasets for robust training and…