Can large language models reason about medical questions?
This addresses the problem of assessing AI reasoning in real-world medical scenarios for researchers and practitioners, but it is incremental as it applies existing methods to new data.
The study evaluated large language models on medical reasoning benchmarks, finding that GPT-3.5 achieved passing scores of 60.2% on MedQA-USMLE, 62.7% on MedMCQA, and 78.2% on PubMedQA, while Llama-2 70B also passed MedQA-USMLE with 62.5% accuracy.
Although large language models (LLMs) often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge. We set out to investigate whether close- and open-source models (GPT-3.5, LLama-2, etc.) can be applied to answer and reason about difficult real-world-based questions. We focus on three popular medical benchmarks (MedQA-USMLE, MedMCQA, and PubMedQA) and multiple prompting scenarios: Chain-of-Thought (CoT, think step-by-step), few-shot and retrieval augmentation. Based on an expert annotation of the generated CoTs, we found that InstructGPT can often read, reason and recall expert knowledge. Last, by leveraging advances in prompt engineering (few-shot and ensemble methods), we demonstrated that GPT-3.5 not only yields calibrated predictive distributions, but also reaches the passing score on three datasets: MedQA-USMLE 60.2%, MedMCQA 62.7% and PubMedQA 78.2%. Open-source models are closing the gap: Llama-2 70B also passed the MedQA-USMLE with 62.5% accuracy.