Refining the Responses of LLMs by Themselves
This addresses the challenge of enhancing LLM performance for users without access to advanced models, though it appears incremental as it builds on existing prompt engineering techniques.
The paper tackles the problem of improving large language model (LLM) output quality by proposing a prompt engineering approach where the LLM iteratively refines its own responses without external models, achieving results comparable to or better than GPT-4 with GPT-3.5.
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.