Authors: Clément 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

Published on: April 23, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2404.14779

Summary

• What is new: The study introduces Med42, a medical Large Language Model (LLM) with enhanced retrieval, reasoning, and question-answering capabilities, achieving a 72% accuracy on USMLE datasets.
• Why this is important: Identifying the most effective fine-tuning methodology for medical LLMs to improve knowledge retrieval and question-answering capabilities.
• What the research proposes: Comparative analysis of full-parameter fine-tuning and parameter-efficient tuning on LLMs specifically designed for the medical field.
• Results: Med42 sets a new performance standard for medical LLMs with its high accuracy rate on well-known medical benchmarks.

Technical Details

Technological frameworks used: Llama-2 architecture

Models used: Developed and refined series of LLMs specifically for medical applications

Data used: USMLE datasets and various medical benchmarks

Potential Impact

Healthcare providers, medical education platforms, and AI-driven healthcare application developers

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