The advent of large language models (LLMs) such as ChatGPT has attracted
considerable attention in various domains due to their remarkable performance
and versatility. As the use of these models continues to grow, the importance
of effective prompt engineering has come to the fore. Prompt optimization
emerges as a crucial challenge, as it has a direct impact on model performance
and the extraction of relevant information. Recently, evolutionary algorithms
(EAs) have shown promise in addressing this issue, paving the way for novel
optimization strategies. In this work, we propose a evolutionary
multi-objective (EMO) approach specifically tailored for prompt optimization
called EMO-Prompts, using sentiment analysis as a case study. We use sentiment
analysis capabilities as our experimental targets. Our results demonstrate that
EMO-Prompts effectively generates prompts capable of guiding the LLM to produce
texts embodying two conflicting emotions simultaneously.

我们提出了一种特定于提示优化的进化多目标方法（EMO-Prompts），以情感分析为案例研究，结果表明 EMO-Prompts 可以有效生成能够引导大型语言模型同时表达两种相互冲突情感的提示。