Elodie Ferreres


2024

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Efficient Medical Question Answering with Knowledge-Augmented Question Generation
Julien Khlaut | Corentin Dancette | Elodie Ferreres | Benani Alaedine | Herent Herent | Pierre Manceron
Proceedings of the 6th Clinical Natural Language Processing Workshop

In the expanding field of language model applications, medical knowledge representation remains a significant challenge due to the specialized nature of the domain. Large language models, such as GPT-4, obtain reasonable scores on medical question-answering tasks, but smaller models are far behind.In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. We first fine-tune the model on a corpus of medical textbooks. Then, we use GPT-4 to generate questions similar to the downstream task, prompted with textbook knowledge, and use them to fine-tune the model. Additionally, we introduce ECN-QA, a novel Medical QA dataset containing “progressive questions” composed of related sequential questions. We show the benefits of our training strategy on this dataset.The study’s findings highlight the potential of small language models in the medical domain when appropriately fine-tuned.