Refining the Responses of LLMs by Themselves
Tianqiang Yan Tiansheng Xu
In this paper, we propose a simple yet efficient approach based on prompt
engineering that leverages the large language model itself to optimize its
answers without relying on auxiliary models. We introduce an iterative
self-evaluating optimization mechanism, with the potential for improved output
quality as iterations progress, removing the need for manual intervention. The
experiment's findings indicate that utilizing our response refinement framework
on the GPT-3.5 model yields results that are on par with, or even surpass,
those generated by the cutting-edge GPT-4 model. Detailed implementation
strategies and illustrative examples are provided to demonstrate the
superiority of our proposed solution.