@inproceedings{
hu2023evoke,
title={Evoke: Evoking Critical Thinking Abilities in {LLM}s via Reviewer-Author Prompt Editing},
author={Xinyu Hu and Pengfei Tang and Simiao Zuo and Zihan Wang and Bowen Song and Qiang Lou and Jian Jiao and Denis X Charles},
booktitle={R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models},
year={2023},
url={https://openreview.net/forum?id=yMkalE52Xv}
}
Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable responses. However, the potential of LLMs are not fully harnessed by commonly-used prompting methods: many human-in-the-loop algorithms employ ad-hoc procedures for prompt selection; while auto prompt generation approaches are essentially searching all possible prompts randomly and inefficiently. We propose Evoke, an automatic prompt refinement framework. In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback. Such an author-reviewer feedback loop ensures that the prompt is refined in each iteration.  We further aggregate a data selection approach to Evoke, where only the hard samples are exposed to the LLM. The hard samples are more important because the LLM can develop deeper understanding of the tasks out of them, while the model may already know how to solve the easier cases. Experimental results show that Evoke significantly outperforms existing methods across a diverse range of tasks.
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
Xinyu Hu Pengfei Tang Simiao Zuo Zihan Wang Bowen Song Qiang Lou Jian Jiao Denis X Charles