Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study
Large language models (LLMs) have achieved strong performance on medical question answering (medical QA), and chain-of-thought (CoT) prompting has further improved results by eliciting explicit intermediate reasoning; meanwhile, self-reflective (self-corrective) prompting has been widely claimed to enhance model reliability by prompting LLMs to critique and revise their own reasoning, yet its effectiveness in safety-critical medical settings remains unclear. In this work, we conduct an exploratory analysis of self-reflective reasoning for medical multiple-choice question answering: using GPT-4o and GPT-4o-mini, we compare standard CoT prompting with an iterative self-reflection loop and t...
| Authors | Zaifu Zhan, Mengyuan Cui, Rui Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 31 March 2026 |
| Last updated at source | 2 April 2026 |
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