O_FT@EvalLLM2025 : \'etude comparative de choix de donn\'ees et de strat\'egies d'apprentissage pour l'adaptation de mod\`eles de langue \`a un domaine

TL;DR

This study explores domain-specific adaptation of a language model to the defense sector using classical fine-tuning techniques, demonstrating improved domain knowledge and task performance with a focus on sustainability.

Contribution

It presents a comprehensive data collection, generation, and selection process for domain adaptation of a relatively small language model, showing effective results in the defense domain.

Findings

Enhanced domain-specific knowledge and task handling in the adapted models.

Comparable or superior performance on general knowledge tasks.

Feasibility of domain adaptation for small models considering carbon footprint.

Abstract

This paper presents the work carried out by the O_FT team, joint with Orange and Ouest-France, on adapting language models to the defense domain as part of the EvalLLM2025 challenge. This work focused on adapting the \texttt{Mistral-7B-Instruct-v0.3} model using classical techniques of continued pre-training and instruction-tuning. The core of our efforts is based on collecting, generating, and selecting data for these two stages as well as for model evaluation. Experiments show that our adapted models have better domain-specific knowledge and improved domain-specific task processing skills, along with comparable (or even superior) performance on general knowledge and skills. Considering the carbon footprint of our adaptations, this work demonstrates the feasibility of domain adaptation for relatively small models. -- Ce document pr\'esente les travaux r\'ealis\'es par l'\'equipe O_FT conjointe \`a Orange et Ouest-France sur l'adaptation de mod\`eles de langue au domaine de la d\'efense dans le cadre du challenge EvalLLM2025. Ces travaux se sont concentr\'es sur l'adaptation du mod\`ele \texttt{Mistral-7B-Instruct-v0.3} avec des techniques classiques de poursuite du pr\'e-entra\^inement et d'affinage sur instructions. L'essentiel de nos travaux a port\'e sur la constitution, g\'en\'eration et s\'election de donn\'ees pour ces deux \'etapes ainsi que pour l'\'evaluation des mod\`eles. Les exp\'eriences montrent que nos mod\`eles adapt\'es ont de meilleures de connaissances de fond et une meilleure capacit\'e de traitement de t\^aches sur le domaine de la d\'efense, ainsi que des performances comparables (voire sup\'erieures) sur des connaissances ou capacit\'es g\'en\'eralistes. Mis au regard des empreintes carbones de nos adaptations, ces travaux d\'emontrent ainsi la viabilit\'e de l'adaptation \`a un domaine de mod\`eles relativement petits.