Auto-Evolve: Enhancing Large Language Model's Performance via
Self-Reasoning Framework
Auto-Evolve: Enhancing Large Language Model's Performance via
Self-Reasoning Framework
Recent advancements in prompt engineering strategies, such as Chain-of-Thought (CoT) and Self-Discover, have demonstrated significant potential in improving the reasoning abilities of Large Language Models (LLMs). However, these state-of-the-art (SOTA) prompting strategies rely on single or fixed set of static seed reasoning modules like \emph{"think step by step"} or \emph{"break …