While self-correction has shown promise in improving LLM outputs in terms of style and quality, recent attempts to self-correct logical or reasoning errors often cause correct answers to become incorrect, resulting in worse performances overall.
In this paper, we break down the self-correction process into two core components:mistake findingandoutput correction. For mistake finding, we release BIG-Bench Mistake, a dataset of logical mistakes in Chain-of-Thought reasoning traces. We provide benchmark numbers for several state-of-the-art LLMs, and demonstrate that LLMs generally struggle with finding logical mistakes. For output correction, we propose a backtracking method which provides large improvements when given information on mistake location. We construe backtracking as a lightweight alternative to reinforcement learning methods, and show that it remains effective with a reward model at 60-70% accuracy.