Meta Reasoning for Large Language Models

TL;DR

Meta-Reasoning Prompting (MRP) enables large language models to dynamically select and apply appropriate reasoning methods for diverse tasks, improving performance and efficiency by mimicking human meta-reasoning strategies.

Contribution

The paper introduces MRP, a novel prompting approach that guides LLMs to adaptively choose reasoning strategies based on task cues, advancing beyond static in-context learning methods.

Findings

MRP achieves or approaches state-of-the-art performance across various benchmarks.

Dynamic reasoning selection improves LLM adaptability and efficiency.

MRP demonstrates broad applicability to complex, diverse problem domains.

Abstract

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.