Self-Regeneration: Step-Level Regeneration Improves the LLM's Causal Reasoning Ability

Large Language Models (LLMs) have performed remarkably on different tasks. The discovery of self-evolution approaches reveals that LLMs can think like humans which improves their reasoning abilities without external assistance. In this paper, we introduce a step-level framework called Self-Regeneration that can assist LLMs in deciding the consistency of each step by regenerating and giving out the confidence of responses. We select the casual reasoning domain and test Self-Regeneration on some popular LLMs. The result shows our approach improves LLMs' reasoning ability and increases the accuracy of responses.