Can Separators Improve Chain-of-Thought Prompting?
Yoonjeong Park Hyunjin Kim Chanyeol Choi Junseong Kim Jy-yong Sohn
Chain-of-thought (CoT) prompting is a simple and effective method for
improving the reasoning capabilities of Large language models (LLMs). The basic
idea of CoT is to let LLMs break down their thought processes step-by-step by
putting exemplars in the input prompt. However, the densely structured prompt
exemplars of CoT may cause the cognitive overload of LLMs. Inspired by human
cognition, we introduce CoT-Sep, a novel method that strategically employs
separators at the end of each exemplar in CoT prompting. These separators are
designed to help the LLMs understand their thought processes better while
reasoning. It turns out that CoT-Sep significantly improves the LLMs'
performances on complex reasoning tasks (e.g., GSM-8K, AQuA, CSQA), compared
with the vanilla CoT, which does not use separators. We also study the effects
of the type and the location of separators tested on multiple LLMs, including
GPT-3.5-Turbo, GPT-4, and LLaMA-2 7B. Interestingly, the type/location of
separators should be chosen appropriately to boost the reasoning capability of
CoT.