基于聚焦指令的启发式搜索算法的自动提示优化综述
A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm
AI总结：
本综述系统总结了基于启发式搜索算法的自动提示优化方法，提出按优化位置、对象、标准、算子和搜索算法分类的全面分类法，并介绍了相关数据集与工具及未来挑战。
摘要：
大型语言模型的最新进展在各种自然语言处理任务中取得了显著成就，使得提示工程在引导模型输出方面日益核心。尽管手动方法可能有效，但它们通常依赖直觉，且不会随时间推移自动优化提示。相比之下，采用基于启发式搜索算法的自动提示优化能够以最少的人工干预系统地探索并改进提示。本综述提出了这些方法的全面分类法，按优化发生位置、优化对象、驱动优化的标准、生成新提示的算子以及应用的迭代搜索算法对其进行分类。我们进一步强调了支持并加速自动化提示优化的专用数据集和工具。最后，我们讨论了关键的开放挑战，指明了未来实现更稳健且多功能LLM应用的机遇。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
