Evaluating Medical Accuracy in LLMs
Isolating factual medical knowledge from reasoning capabilities
This research evaluates LLMs' ability to recall and apply factual medical knowledge using structured one-hop judgment tasks from UMLS data.
- Created a specialized benchmark isolating knowledge from reasoning abilities
- Evaluated multiple LLMs including GPT-4, Claude, and Llama models
- Found significant gaps between human expert knowledge and even advanced LLMs
- Identified that LLMs still struggle with factual precision in high-stakes medical domains
This matters for medical applications because inaccurate information in healthcare settings can lead to critical consequences for patient safety. The findings highlight the need for careful validation before deploying LLMs in clinical decision support.