Evaluating base and retrieval augmented LLMs with document or online support for evidence based neurology

Evaluating base and retrieval augmented LLMs with document or online support for evidence based neurology

Blog Article

Abstract Effectively managing evidence-based information is increasingly challenging.This study tested large language models (LLMs), including document- and online-enabled retrieval-augmented generation kaiser copy stands (RAG) systems, using 13 recent neurology guidelines across 130 questions.Results showed substantial variability.

RAG improved accuracy compared to base models but still produced potentially harmful answers.RAG-based systems performed worse on case-based than knowledge-based questions.Further refinement and improved regulation is needed for apac1/60/1/cw safe clinical integration of RAG-enhanced LLMs.