Self-Correction Bench: Revealing and Addressing the Self-Correction
  Blind Spot in LLMs
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.
68678e2a11736b002cf34d5e
Ken Tsui
claimed_verified
2025-07-05T07:53:06.330Z
60e50ce5350d181892d5a636
https://cdn-avatars.huggingface.co/v1/production/uploads/60e50ce5350d181892d5a636/qjYikZtE-2kjub-6XG5Kn.jpeg
Ken Tsui
user
kenhktsui
Self-Correction Bench: Revealing and Addressing the Self-Correction
  Blind Spot in LLMs
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.
LLMs exhibit a 'Self-Correction Blind Spot' where they fail to correct errors in their own outputs, which can be mitigated by appending "Wait" and is related to the composition of training data.
large language models LLMs self-correction autoregressive LLMs self-correction blind spot self-correction bench error injection training data composition human training demonstrations RL-trained models error correction outcome feedback