@inproceedings{ramnath-etal-2025-systematic,
    title = "A Systematic Survey of Automatic Prompt Optimization Techniques",
    author = "Ramnath, Kiran  and
      Zhou, Kang  and
      Guan, Sheng  and
      Mishra, Soumya Smruti  and
      Qi, Xuan  and
      Shen, Zhengyuan  and
      Wang, Shuai  and
      Woo, Sangmin  and
      Jeoung, Sullam  and
      Wang, Yawei  and
      Wang, Haozhu  and
      Ding, Han  and
      Lu, Yuzhe  and
      Xu, Zhichao  and
      Zhou, Yun  and
      Srinivasan, Balasubramaniam  and
      Yan, Qiaojing  and
      Chen, Yueyan  and
      Ding, Haibo  and
      Xu, Panpan  and
      Cheong, Lin Lee",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1681/",
    pages = "33066--33098",
    ISBN = "979-8-89176-332-6",
    abstract = "Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. However, prompt engineering remains an impediment for end users due to rapid advances in models, tasks, and associated best practices. To mitigate this, Automatic Prompt Optimization (APO) techniques have recently emerged that use various automated techniques to help improve the performance of LLMs on various tasks. In this paper, we present a comprehensive survey summarizing the current progress and remaining challenges in this field. We provide a formal definition of APO, a 5-part unifying framework, and then proceed to rigorously categorize all relevant works based on their salient features therein. We hope to spur further research guided by our framework."
}