Self-reflective prompting (self-correction) fails to improve accuracy in safety-critical medical QA, frequently introducing new errors rather than fixing old ones.
April 2, 2026
Original Paper
Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study
arXiv · 2604.00261
The Takeaway
It provides a necessary reality check for 'agentic' workflows that rely on self-reflection loops for reliability. For practitioners in high-stakes domains, this proves that reasoning transparency does not equal reasoning correctness and that self-correction is not a substitute for robust base model performance.
From the abstract
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 explorato