Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs Parameter-Efficient Approaches
<|reference_start|>Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs Parameter-Efficient Approaches: This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the context of medical Large Language Models (LLMs). We developed and refined a series of LLMs, based on the Llama-2 architecture, specifically designed to enhance medical knowledge retrieval, reasoning, and question-answering capabilities. Our experiments systematically evaluate the effectiveness of these tuning strategies across various well-known medical benchmarks. Notably, our medical LLM Med42 showed an accuracy level of 72% on the US Medical Licensing Examination (USMLE) datasets, setting a new standard in performance for openly available medical LLMs. Through this comparative analysis, we aim to identify the most effective and efficient method for fine-tuning LLMs in the medical domain, thereby contributing significantly to the advancement of AI-driven healthcare applications.<|reference_end|>
@article{christophe2024med42,
    title={Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs:
  Full-Parameter vs. Parameter-Efficient Approaches},
    author={Cl'ement Christophe, Praveen K Kanithi, Prateek Munjal, Tathagata
  Raha, Nasir Hayat, Ronnie Rajan, Ahmed Al-Mahrooqi, Avani Gupta, Muhammad
  Umar Salman, Gurpreet Gosal, Bhargav Kanakiya, Charles Chen, Natalia
  Vassilieva, Boulbaba Ben Amor, Marco AF Pimentel, Shadab Khan},
    journal={arXiv preprint arXiv:2404.14779},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14779},
    primaryClass={cs.CL}
}