Morgan Veyret | Searcharxiv

arXiv2025

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

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.

cs.CL↗

arXiv2024

Exploring ReAct Prompting for Task-Oriented Dialogue: Insights and Shortcomings

Large language models (LLMs) gained immense popularity due to their impressive capabilities in unstructured conversations. Empowering LLMs with advanced prompting strategies such as reasoning and acting (ReAct) (Yao et al., 2022) has shown promise in solving complex tasks traditionally requiring reinforcement learning. In this work, we apply the ReAct strategy to guide LLMs performing task-oriented dialogue (TOD). We evaluate ReAct-based LLMs (ReAct-LLMs) both in simulation and with real users. While ReAct-LLMs severely underperform state-of-the-art approaches on success rate in simulation, this difference becomes less pronounced in human evaluation. Moreover, compared to the baseline, humans report higher subjective satisfaction with ReAct-LLM despite its lower success rate, most likely thanks to its natural and confidently phrased responses.

cs.CL↗

arXiv2024

WEBDial, a Multi-domain, Multitask Statistical Dialogue Framework with RDF

Typically available dialogue frameworks have adopted a semantic representation based on dialogue-acts and slot-value pairs. Despite its simplicity, this representation has disadvantages such as the lack of expressivity, scalability and explainability. We present WEBDial: a dialogue framework that relies on a graph formalism by using RDF triples instead of slot-value pairs. We describe its overall architecture and the graph-based semantic representation. We show its applicability from simple to complex applications, by varying the complexity of domains and tasks: from single domain and tasks to multiple domains and complex tasks.

cs.CL↗