Core Concepts
Prompt engineering can enhance OS LLM performance in medical question-answering.
Abstract
Abstract: Discusses the significance of OS models in medical LLMs and introduces OpenMedLM.
Methods: Evaluation of OS LLMs on medical benchmarks using various prompting strategies.
Results: OpenMedLM outperforms previous OS models on medical benchmarks through prompt engineering.
Discussion: Highlights the potential of generalist OS LLMs in healthcare tasks and the importance of prompt engineering.
Conclusion: OpenMedLM showcases the effectiveness of prompt engineering for medical applications.
Stats
"The model delivers a 72.6% accuracy on the MedQA benchmark."
"Achieves 81.7% accuracy on the MMLU medical-subset."
Quotes
"Prompt engineering can outperform fine-tuning in medical question-answering."
"OpenMedLM showcases the benefits of leveraging prompt engineering for medical LLMs."