Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns

TL;DR

This paper introduces a method to improve Chain of Thought prompting in large language models by using reasoning patterns, which enhances robustness, interpretability, and performance across reasoning tasks.

Contribution

It proposes leveraging reasoning patterns and task-specific sets to select diverse demonstrations, improving CoT prompting effectiveness and interpretability.

Findings

Enhanced robustness across reasoning tasks

Improved performance over existing methods

Provides explicit interpretability of reasoning processes

Abstract

Chain of Thought (CoT) prompting can encourage language models to engage in multi-step logical reasoning. The quality of the provided demonstrations significantly influences the success of downstream inference tasks. Current unsupervised CoT methods primarily select examples based on the semantics of the questions, which can introduce noise and lack interpretability. In this paper, we propose leveraging reasoning patterns to enhance CoT prompting effectiveness. Reasoning patterns represent the process by which language models arrive at their final results. By utilizing prior knowledge and prompt-based methods from large models, we first construct task-specific pattern sets. We then select diverse demonstrations based on different reasoning patterns. This approach not only mitigates the impact of noise but also provides explicit interpretability to help us understand the mechanisms of CoT. Extensive experiments demonstrate that our method is more robust and consistently leads to improvements across various reasoning tasks.