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.

翻译：思维链提示是一种简单有效的方法，用于提升大型语言模型的推理能力。其基本思想是通过在输入提示中提供示例，让模型逐步分解思考过程。然而，思维链中密集排列的提示示例可能导致模型出现认知过载。受人类认知启发，我们提出CoT-Sep这一创新方法，该方法在思维链提示的每个示例末尾策略性地使用分隔符。这些分隔符旨在帮助模型在推理时更好地理解其思考过程。实验表明，与未使用分隔符的标准思维链相比，CoT-Sep显著提升了模型在复杂推理任务（如GSM-8K、AQuA、CSQA）上的性能。我们还研究了分隔符类型和位置对多个大型语言模型（包括GPT-3.5-Turbo、GPT-4和LLaMA-2 7B）的影响。有趣的是，需要恰当选择分隔符的类型/位置才能增强思维链的推理能力。