Learning How to Prompt with Large Language Models
Authors: Qiwei Zhao, Jixuan Leng, Xujiang Zhao, Shiqi Lou, Haifeng Chen, Huaxiu Yao


Reviewer paper summaries: This paper proposes a prompt optimization method that utilizes different prompt optimization trajectories for the same task. The authors believe that there are commonalities in the optimization of different prompts. Therefore, they suggest using LLM and COT to summarize the optimization trajectories of different prompts, forming a meta prompt to guide subsequent prompt optimization. Experiments were conducted on toxicity, summarization, and simplification tasks.
Summary of strengths: Using commonalities  in the optimization of different prompts as the meta prompts is valuable.
Summary of weaknesses: 1. The experiments are not convincing. (a) The dataset used in the experiments is not a common dataset for prompt optimization. It is recommended to add some common tasks for comparison. (b) There is a lack of necessary experimental details, such as implementation details of the baseline, the number of optimization rounds, and other hyperparameters. (c) The experimental results are somewhat strange. For example, why does the new prompt perform worse than the original? It is better to further analysis the results.
2. The writing needs improvement.
Comments, suggestions and typos: Line 227: Algorithm 10 -> Algorithm 1
Line 368: We -> we
Overall assessment scores: 2