Evolutionary Multi-Objective Optimization of Large Language Model Prompts for Balancing Sentiments
This addresses prompt engineering for LLMs in sentiment analysis, but it appears incremental as it applies known evolutionary methods to a specific domain.
The paper tackles the challenge of optimizing prompts for large language models to balance conflicting sentiments, proposing an evolutionary multi-objective approach called EMO-Prompts that effectively generates prompts for producing texts with two conflicting emotions simultaneously.
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.