Papers › Can large language models reason about medical questions?
Can large language models reason about medical questions?
Valentin Liévin, Christoffer Egeberg Hother, Andreas Geert Motzfeldt, Ole Winther
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.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multiple Choice Question Answering (MCQA) | MedMCQA | Codex 5-shot CoT | Dev Set (Acc-%) | 0.597 | #5 of 22 | Archive leaderboard | report |
| Multiple Choice Question Answering (MCQA) | MedMCQA | Codex 5-shot CoT | Test Set (Acc-%) | 0.627 | #5 of 22 | Archive leaderboard | report |
| Question Answering | MedQA | Codex 5-shot CoT | Accuracy | 60.2 | #13 of 27 | Archive leaderboard | report |
| Question Answering | PubMedQA | Codex 5-shot CoT | Accuracy | 78.2 | #7 of 30 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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