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Arxiv
2d
292
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Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs
- Large language models (LLMs) can make mistakes and struggle with self-correction, leading to a 'Self-Correction Blind Spot.'
- Researchers introduce the Self-Correction Bench framework to measure the blind spot by injecting controlled errors at varying complexity levels.
- Testing 14 models revealed an average blind spot rate of 64.5%, with training data composition playing a crucial role in this limitation.
- Appending the word 'Wait' reduced blind spots by 89.3%, showing potential for improving the reliability and trustworthiness of LLMs.
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