Pattern-Aware Chain-of-Thought Prompting in Large Language Models

arXiv:2404.14812v1 Announce Type: new
Abstract: 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 …

abstract accuracy arxiv automated cs.cl guide impacts inference language language models large language large language models patterns prompting quality reasoning semantics show success tasks thought type