Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs

• URL: http://arxiv.org/abs/2507.02778v1
• Date: Thu, 03 Jul 2025 16:41:30 GMT
• Title: Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs
• Authors: Ken Tsui,
• Abstract summary: Self-correction is an important capability for large language models (LLMs)<n>While LLMs can identify error in user input, they exhibit a systematic 'Self-Correction Blind Spot'<n>Testing 14 models, we find an average 64.5% blind spot rate.<n>Remarkably, simply appending "Wait" reduces blind spots by 89.3%, suggesting that the capability exists but requires activation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although large language models (LLMs) have become transformative, they still make mistakes and can explore unproductive reasoning paths. Self-correction is an important capability for a trustworthy LLM, particularly an autoregressive LLM. While LLMs can identify error in user input, they exhibit a systematic 'Self-Correction Blind Spot' - failing to correct identical error in their own outputs. To systematically study this phenomenon, we introduce Self-Correction Bench, a systematic framework to measure this phenomenon through controlled error injection at three complexity levels. Testing 14 models, we find an average 64.5% blind spot rate. We find multiple evidences that this limitation relates to training data composition: human training demonstrations predominantly show error-free responses rather than error-correction sequences, unlike RL-trained models that learn error correction through outcome feedback. Remarkably, simply appending "Wait" reduces blind spots by 89.3%, suggesting that the capability exists but requires activation. Our work highlights a critical limitation in current LLMs and offers potential avenues for improving their reliability and trustworthiness.

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