We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system
prompting method for large language models (LLMs) inspired by human
meta-reasoning. Traditional in-context learning-based reasoning techniques,
such as Tree-of-Thoughts, show promise but lack consistent state-of-the-art
performance across diverse tasks due to their specialized nature. MRP addresses
this limitation by guiding LLMs to dynamically select and apply different
reasoning methods based on the specific requirements of each task, optimizing
both performance and computational efficiency. With MRP, LLM reasoning operates
in two phases. Initially, the LLM identifies the most appropriate reasoning
method using task input cues and objective descriptions of available methods.
Subsequently, it applies the chosen method to complete the task. This dynamic
strategy mirrors human meta-reasoning, allowing the model to excel in a wide
range of problem domains. We evaluate the effectiveness of MRP through
comprehensive benchmarks. The results demonstrate that MRP achieves or
approaches state-of-the-art performance across diverse tasks. MRP represents a
significant advancement in enabling LLMs to identify cognitive challenges
across problems and leverage benefits across different reasoning approaches,
enhancing their ability to handle diverse and complex problem domains
efficiently. Every LLM deserves a Meta-Reasoning Prompting to unlock its full
potential and ensure adaptability in an ever-evolving landscape of challenges
and applications.

Meta-Reasoning Prompting（MRP）是一种受人类元推理启发的用于大型语言模型（LLMs）的新型高效系统提示方法，通过动态选择和应用不同的推理方法来优化性能和计算效率，实现了在不同任务中达到或接近最先进性能的结果。