系统梳理自动提示优化方法，助力大模型更精准输出
A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm
- 按优化位置、目标、标准等五维度构建分类体系
- 提出基于启发式搜索的自动优化框架，减少人工干预
- 适合想提升大模型提示工程效率的研究者与开发者
大语言模型在自然语言处理任务中取得显著进展，使提示工程成为引导模型输出的核心手段。尽管手动方法有效，但依赖直觉且难以持续优化。相比之下，基于启发式搜索的自动提示优化方法可在较少人工干预下系统探索并改进提示。本文提出一个全面的分类体系，从优化位置、优化目标、优化准则、提示生成算子和迭代搜索算法五个方面对现有方法进行归类。同时，文章还介绍了支持自动化提示优化的专用数据集与工具。最后，讨论了关键开放挑战，展望未来更鲁棒、更通用的大模型应用前景。
原文摘要 · Abstract (English)
Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods can be effective, they typically rely on intuition and do not automatically refine prompts over time. In contrast, automatic prompt optimization employing heuristic-based search algorithms can systematically explore and improve prompts with minimal human oversight. This survey proposes a comprehensive taxonomy of these methods, categorizing them by where optimization occurs, what is optimized, what criteria drive the optimization, which operators generate new prompts, and which iterative search algorithms are applied. We further highlight specialized datasets and tools that support and accelerate automated prompt refinement. We conclude by discussing key open challenges pointing toward future opportunities for more robust and versatile LLM applications.
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