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Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches
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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.
Forward citations
Cited by 4 Pith papers
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AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification
An entropy-guided reward that shortens answers to easy, confident questions and extends reasoning on hard ones reduces medical QA tokens by 4.7x to 6.4x with minimal accuracy loss.
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Automating Expert-Level Medical Reasoning Evaluation of Large Language Models
A benchmark with expert-annotated reasoning steps and an LLM judge that scores reasoning by step coverage, showing strong correlation with expert evaluation.
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Collaboration among Multiple Large Language Models for Medical Question Answering
An iterative collaboration framework where three LLMs exchange summarized reasoning on disagreed questions raises USMLE-style answer accuracy by 5.2 to 6.6 percentage points per model and raises consensus from 51% to 83%.
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Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL
Medical QA accuracy improves substantially through reinforcement learning with a binary correct-answer reward alone, without supervised fine-tuning on distilled reasoning traces.
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