  Chain-of-thought (CoT) prompting can guide language models to engage in
complex multi-step reasoning. The quality of provided demonstrations
significantly impacts the success of downstream inference tasks. While existing
automated methods prioritize accuracy and semantics in these demonstrations, we
show that the underlying reasoning patterns play a more crucial role in such
tasks. In this paper, we propose Pattern-Aware CoT, a prompting method that
considers the diversity of demonstration patterns. By incorporating patterns
such as step length and reasoning process within intermediate steps, PA-CoT
effectively mitigates the issue of bias induced by demonstrations and enables
better generalization to diverse scenarios. We conduct experiments on nine
reasoning benchmark tasks using two open-source LLMs. The results show that our
method substantially enhances reasoning performance and exhibits robustness to
errors. The code will be made publicly available.
