Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in Large Language Models

• URL: http://arxiv.org/abs/2507.02778v2
• Date: Sat, 04 Oct 2025 08:57:59 GMT
• Title: Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in Large Language Models
• Authors: Ken Tsui,
• Abstract summary: Large language models (LLMs) make mistakes and can explore unproductive reasoning paths.<n>Self-correction capability is essential for deploying LLMs in safety-critical applications.<n>We uncover a systematic failure: LLMs cannot correct errors in their own outputs while successfully correcting identical errors from external sources.
• Score: 0.7910367295422812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although large language models (LLMs) have transformed AI, they still make mistakes and can explore unproductive reasoning paths. Self-correction capability is essential for deploying LLMs in safety-critical applications. We uncover a systematic failure: LLMs cannot correct errors in their own outputs while successfully correcting identical errors from external sources - a limitation we term the Self-Correction Blind Spot. To study this phenomenon, we introduce Self-Correction Bench, an evaluation framework to measure this phenomenon through controlled error injection at three complexity levels. Testing 14 open-source non-reasoning models, we find an average 64.5% blind spot rate. We provide multiple lines of evidence suggesting this limitation may be influenced by training data: human demonstrations rarely include error-correction sequences (favoring error-free responses), whereas reinforcement learning (RL) trained models learn error correction via outcome feedback. Remarkably, appending a minimal "Wait" prompt activates a 89.3% reduction in blind spots, suggesting dormant capabilities that require triggering. Our work highlights a critical limitation potentially influenced by training distribution and offers a practical approach to enhance LLM reliability and trustworthiness - vital for safety-critical domains.

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