Can Separators Improve Chain-of-Thought Prompting?
This is an incremental improvement for enhancing reasoning in large language models, addressing a specific bottleneck in prompt design.
The paper tackles the problem of cognitive overload in chain-of-thought prompting for large language models by introducing COT-SEP, which uses separators in exemplars, resulting in significant performance improvements on complex reasoning tasks like GSM8K, AQuA, and CSQA compared to vanilla CoT.
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 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. Interestingly, it turns out that COT-SEP significantly improves the LLMs' performances on complex reasoning tasks (e.g., GSM8K, 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.