Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction

Simon Meoni, Éric de la Clergerie, Théo Ryffel

Abstract: In clinical and other specialized domains, data are scarce due to their confidential nature. This lack of data is a major problem when fine-tuning language models. Nevertheless, very large language models (LLMs) are promising for the medical domain but cannot be used directly in healthcare facilities due to data confidentiality issues. We explore an approach of annotating training data with LLMs to train smaller models more adapted to our problem. We show that this method yields promising results for information extraction tasks.