@inproceedings{cui-etal-2025-heuristic,
    title = "Heuristic-based Search Algorithm in Automatic Instruction-focused Prompt Optimization: A Survey",
    author = "Cui, Wendi  and
      Zhang, Jiaxin  and
      Li, Zhuohang  and
      Sun, Hao  and
      Lopez, Damien  and
      Das, Kamalika  and
      Malin, Bradley A.  and
      Kumar, Sricharan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.1140/",
    pages = "22093--22111",
    ISBN = "979-8-89176-256-5",
    abstract = "Recent advances in Large Language Models(LLMs) have led to remarkable achievements across a variety of Natural Language Processing(NLP) tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods (e.g., ``chain-of-thought,'' ``step-by-step'' prompts) 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."
}