20 citations · 116 across the 17 of their papers we have counts for
6 papers · 1 filter
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks
Yanis Labrak, Mickael Rouvier, Richard Dufour
We evaluate four state-of-the-art instruction-tuned large language models (LLMs) -- ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca -- on a set of 13 real-world clinical and biomedic…
The Role of Global and Local Context in Named Entity Recognition
Arthur Amalvy, Vincent Labatut, Richard Dufour
Pre-trained transformer-based models have recently shown great performance when applied to Named Entity Recognition (NER). As the complexity of their self-attention mechanism preve…
DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains
Yanis Labrak, Adrien Bazoge, Richard Dufour +4
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…
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain
Yanis Labrak, Adrien Bazoge, Richard Dufour +4
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions…
Text revision in Scientific Writing Assistance: An Overview
Léane Jourdan, Florian Boudin, Richard Dufour +1
Writing a scientific article is a challenging task as it is a highly codified genre. Good writing skills are essential to properly convey ideas and results of research work. Since…
Data Augmentation for Robust Character Detection in Fantasy Novels
Arthur Amalvy, Vincent Labatut, Richard Dufour
Named Entity Recognition (NER) is a low-level task often used as a foundation for solving higher level NLP problems. In the context of character detection in novels, NER false nega…