From Generalist to Specialist: Improving Large Language Models for Medical Physics Using ARCoT

• URL: http://arxiv.org/abs/2405.11040v1
• Date: Fri, 17 May 2024 18:31:38 GMT
• Title: From Generalist to Specialist: Improving Large Language Models for Medical Physics Using ARCoT
• Authors: Jace Grandinetti, Rafe McBeth,
• Abstract summary: ARCoT (Adaptable Retrieval-based Chain of Thought) is a framework designed to enhance the domain-specific accuracy of Large Language Models (LLMs) Our model outperformed standard LLMs and reported average human performance, demonstrating improvements of up to 68%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have achieved remarkable progress, yet their application in specialized fields, such as medical physics, remains challenging due to the need for domain-specific knowledge. This study introduces ARCoT (Adaptable Retrieval-based Chain of Thought), a framework designed to enhance the domain-specific accuracy of LLMs without requiring fine-tuning or extensive retraining. ARCoT integrates a retrieval mechanism to access relevant domain-specific information and employs step-back and chain-of-thought prompting techniques to guide the LLM's reasoning process, ensuring more accurate and context-aware responses. Benchmarking on a medical physics multiple-choice exam, our model outperformed standard LLMs and reported average human performance, demonstrating improvements of up to 68% and achieving a high score of 90%. This method reduces hallucinations and increases domain-specific performance. The versatility and model-agnostic nature of ARCoT make it easily adaptable to various domains, showcasing its significant potential for enhancing the accuracy and reliability of LLMs in specialized fields.

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