Shadab Khan


2024

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Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs.
Clement Christophe | Tathagata Raha | Svetlana Maslenkova | Muhammad Umar Salman | Praveenkumar Kanithi | Marco AF Pimentel | Shadab Khan
Findings of the Association for Computational Linguistics: EMNLP 2024

Large Language Models (LLMs) have demonstrated significant potential in revolutionizing clinical applications. In this study, we investigate the efficacy of four techniques in adapting LLMs for clinical use-cases: continuous pretraining, instruct fine-tuning, NEFTune, and prompt engineering. We employ these methods on Mistral 7B and Mixtral 8x7B models, leveraging a large-scale clinical pretraining dataset of 50 billion tokens and an instruct fine-tuning dataset of 500 million tokens. Our evaluation across various clinical tasks reveals nuanced insights. While continuous pretraining beyond 250 billion tokens yields marginal improvements, instruct fine-tuning emerges as a more influential factor. Notably, NEFTune, designed primarily to enhance generation quality, surprisingly demonstrates additional gains on our benchmark. These findings underscore the importance of tailoring fine-tuning strategies and exploring innovative techniques to optimize LLM performance in the clinical domain.