Slovene instruction-following dataset for large language models GaMS-Instruct-MED-Anatomy 1.0

Read full text
Authors: Emil Plesnik, Robert Tovornik, Borut Fabjan, Filip Korošec, Ines Žabkar, Ema Kuzman, Martin Rigler, Lara Škufca
Cited by: 0

Abstract

GaMS-Instruct-MED-Anatomy is an instruction-following dataset containing 711,805 prompt-response units in Slovene (with English and Latin terminology). The units form a structured, machine-readable database of Slovenian anatomical terminology for training language models. The collection is based on anatomical data collected, translated and validated by medical experts. The data was processed, structured and enriched with automatic scripts and explanations generated using large language models. It includes: • Anatomical terminology in Slovene, English and Latin • SNOMED CT classification (standardized medical coding system) • Classification by body systems • Synonyms and alternative terms (original and generated) • Popular explanations of anatomical structures for the general public • Expert explanations of anatomical structures for medical experts The result is a standardized database in an instructional format that is suitable for use in computational linguistics, natural language processing (NLP), medical informatics and for training and adapting large language models. The corpus is intended for research and development of fine-tuning language models, training and adapting large language models for the medical and anatomical domains, development of medical chatbots and assistants in Slovene, support for healthcare professionals in anatomical terminology, translation of medical documentation, standardization of medical terminology in Slovene, and education in the field of anatomy. For more details on the structure of the dataset, please consult 00README.txt.

Fields of Study & Subjects

Biomedical Text Mining and Ontologies Natural Language Processing Techniques Medical and Biological Sciences

Publication details

Journal / source
Clarin.SI Repository (Clarin.SI)
Publication date
3 February 2026
Language
SL