Pierre-Antoine Gourraud — accepted AI-conference papers

2024

BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains

Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, Richard Dufour

ACL 2024findings

Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the m…

Cited by 217 Source ↗PDF ↗code ↗Scholar ↗

2024

DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain

Yanis Labrak, Adrien Bazoge, Oumaima El Khettari, Mickael Rouvier, Pacome Constant Dit Beaufils, Natalia Grabar +5

COLING 2024main

The biomedical domain has sparked a significant interest in the field of Natural Language Processing (NLP), which has seen substantial advancements with pre-trained language models (PLMs). However, comparing these models has proven challenging due to variations in evaluation protocols across differe…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2023

DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains

Yanis Labrak, Adrien Bazoge, Richard Dufour, Mickael Rouvier, Emmanuel Morin, Béatrice Daille +1

ACL 2023long

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains. In this paper, we propose…

Cited by 53 Source ↗PDF ↗Scholar ↗