Abstract
High-quality prompts are crucial for Large Language Models (LLMs) to achieve exceptional performance. However, manually crafting effective prompts is labor-intensive and demands significant domain expertise, limiting its scalability. Existing automatic prompt optimization methods either extensively explore new prompt candidates, incurring high computational costs due to inefficient searches within a large solution space, or overly exploit feedback on existing prompts, risking suboptimal optimization because of the complex prompt landscape. To address these challenges, we introduce GreenTEA, an agentic LLM workflow for automatic prompt optimization that balances candidate exploration and knowledge exploitation. It leverages a collaborative team of agents to iteratively refine prompts based on feedback from error samples. An analyzing agent identifies common error patterns resulting from the current prompt via topic modeling, and a generation agent revises the prompt to directly address these key deficiencies. This refinement process is guided by a genetic algorithm framework, which simulates natural selection by evolving candidate prompts through operations such as crossover and mutation to progressively optimize model performance. Extensive numerical experiments conducted on public benchmark datasets suggest the superior performance of GreenTEA against human-engineered prompts and existing state-of-the-arts for automatic prompt optimization, covering logical and quantitative reasoning, commonsense, and ethical decision-making.
Abstract (translated)
高质量的提示对于大型语言模型（LLM）实现卓越性能至关重要。然而，手动设计有效的提示既费时又需要深厚的专业知识，这限制了其可扩展性。现有的自动提示优化方法要么广泛探索新的提示候选方案，在庞大的解决方案空间中进行低效搜索导致计算成本高昂；要么过度利用现有提示的反馈，由于复杂的提示景观而面临次优优化的风险。为了解决这些挑战，我们引入了一种名为GreenTEA的代理LLM工作流程，用于自动提示优化，它在候选探索和知识利用之间取得了平衡。 GreenTEA通过一个协作的智能体团队根据错误样本的反馈迭代地改进提示。其中，分析智能体通过主题建模识别当前提示产生的常见错误模式，而生成智能体则修订提示以直接解决这些关键缺陷。该优化过程由遗传算法框架引导，模拟自然选择，在交叉和变异等操作下进化候选提示，逐步优化模型性能。 在公共基准数据集上进行的大量数值实验表明，GreenTEA相对于人为设计的提示以及现有的自动提示优化方法中的最新技术具有更优越的表现，涵盖了逻辑和定量推理、常识及伦理决策等领域。
URL
https://arxiv.org/abs/2508.16603
