Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
This work addresses a bottleneck in reasoning tasks for large language model users, though it is incremental as it builds on existing CoT methods.
The paper tackles the problem of noise and lack of interpretability in unsupervised Chain of Thought prompting by selecting demonstrations based on reasoning patterns, resulting in more robust performance with consistent improvements across various reasoning tasks.
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.