Evolutionary Multi-Objective Optimization of Large Language Model
  Prompts for Balancing Sentiments
Jill Baumann Oliver Kramer
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.