Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study
arXiv:2604.00261v2 Announce Type: new 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 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 track how predictions evolve across reflection steps on three widely used medical QA benchmarks (MedQA, HeadQA, and PubMedQA). We analyze whether self-reflection leads to error correction, error persistence, or the introduction of new errors. Our results
arXiv:2604.00261v2 Announce Type: new 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 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 track how predictions evolve across reflection steps on three widely used medical QA benchmarks (MedQA, HeadQA, and PubMedQA). We analyze whether self-reflection leads to error correction, error persistence, or the introduction of new errors. Our results show that self-reflective prompting does not consistently improve accuracy and its impact is highly dataset- and model-dependent: it yields modest gains on MedQA but provides limited or negative benefits on HeadQA and PubMedQA, and increasing the number of reflection steps does not guarantee better performance. These findings highlight a gap between reasoning transparency and reasoning correctness, suggesting that self-reflective reasoning is better viewed as an analytical tool for understanding model behavior rather than a standalone solution for improving medical QA reliability.
Executive Summary
This exploratory study evaluates the efficacy of self-reflective reasoning in large language models (LLMs) for medical question answering (QA) across three benchmarks (MedQA, HeadQA, PubMedQA) using GPT-4o and GPT-4o-mini. Contrary to claims of enhanced reliability, the research finds that iterative self-reflection does not consistently improve accuracy, with benefits varying by dataset and model. While modest gains are observed in MedQA, performance on HeadQA and PubMedQA either stagnates or deteriorates. The study underscores a critical distinction between reasoning transparency and correctness, positioning self-reflective prompting as a diagnostic tool rather than a reliability solution in safety-critical medical contexts. These findings challenge prevalent assumptions about autonomous error correction in LLMs and highlight the need for nuanced evaluation frameworks in high-stakes domains.
Key Points
- ▸ Self-reflective prompting in LLMs does not uniformly enhance medical QA accuracy, with performance gains contingent on dataset and model architecture.
- ▸ Iterative self-reflection may introduce new errors or fail to address persistent ones, particularly in datasets like HeadQA and PubMedQA.
- ▸ The study reveals a divergence between the transparency of reasoning (enabled by chain-of-thought prompting) and the correctness of outcomes, questioning the reliability of self-correction mechanisms in medical applications.
Merits
Rigorous Empirical Framework
The study employs a robust comparative analysis across three diverse medical QA benchmarks, leveraging state-of-the-art LLMs (GPT-4o and GPT-4o-mini) to isolate the effects of self-reflective prompting. This multi-dimensional evaluation provides granular insights into the technique's variability.
Critical Reassessment of Self-Correction Claims
By systematically testing the efficacy of self-reflection, the authors challenge overstated claims about its reliability in safety-critical domains, offering a necessary counterpoint to anecdotal or proprietary evaluations.
Methodological Innovation in Error Tracking
The study introduces a framework for tracking error evolution across reflection steps, enabling a nuanced understanding of how LLMs navigate and potentially exacerbate or mitigate errors during iterative reasoning.
Demerits
Limited Generalizability of Findings
The analysis is confined to two proprietary LLMs (GPT-4o variants) and three specific medical QA datasets, which may not capture the full spectrum of model architectures or domain-specific challenges in medical QA.
Absence of Human Baseline Comparison
The study lacks a comparison against human expert performance or hybrid human-AI systems, which are critical for contextualizing the practical utility and safety implications of LLM outputs in clinical settings.
Overlooked Confounding Variables
Factors such as prompt engineering nuances, temperature settings, or fine-tuning data biases are not systematically controlled, potentially introducing unaccounted variability in the results.
Expert Commentary
This study makes a seminal contribution to the discourse on AI reliability in medical applications by dismantling the notion that self-reflective reasoning is a panacea for error correction. The authors' nuanced findings reveal a troubling reality: while LLMs can articulate their reasoning processes more transparently, this does not translate into improved correctness, particularly in datasets like HeadQA and PubMedQA, which may mirror the complexity of real-world medical knowledge. The research also implicitly critiques the hype surrounding autonomous AI systems, urging a return to first principles in AI safety—namely, that transparency is not synonymous with reliability. For practitioners and policymakers, the takeaway is clear: self-reflection should be treated as a diagnostic tool to uncover model weaknesses rather than a solution to mitigate them. This work should serve as a cautionary tale for those advocating for unsupervised deployment of LLMs in clinical settings, reinforcing the need for hybrid systems where human expertise remains the ultimate arbitrator of correctness.
Recommendations
- ✓ Conduct further research using open-source LLMs and diverse medical QA datasets to validate the findings across a broader range of architectures and domain-specific challenges.
- ✓ Develop standardized evaluation protocols for medical QA systems that explicitly measure error persistence, correction rates, and the introduction of new errors during iterative reasoning.
- ✓ Explore hybrid human-AI systems where self-reflective prompting is used to flag potential errors for human review, rather than as an autonomous correction mechanism.
- ✓ Collaborate with regulatory agencies to establish benchmarks that assess not just accuracy but also the robustness of reasoning chains under adversarial or edge-case scenarios in medical contexts.
Sources
Original: arXiv - cs.CL
