@inproceedings{zhou-etal-2025-riot,
    title = "{R}i{OT}: Efficient Prompt Refinement with Residual Optimization Tree",
    author = "Zhou, Chenyi  and
      Shi, Zhengyan  and
      Yao, Yuan  and
      Liang, Lei  and
      Chen, Huajun  and
      Zhang, Qiang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1086/",
    pages = "22307--22323",
    ISBN = "979-8-89176-251-0",
    abstract = "Recent advancements in large language models (LLMs) have highlighted their potential across a variety of tasks, but their performance still heavily relies on the design of effective prompts. Existing methods for automatic prompt optimization face two challenges: lack of diversity, limiting the exploration of valuable and innovative directions and semantic drift, where optimizations for one task can degrade performance in others. To address these issues, we propose Residual Optimization Tree (RiOT), a novel framework for automatic prompt optimization. RiOT iteratively refines prompts through text gradients, generating multiple semantically diverse candidates at each step, and selects the best prompt using perplexity. Additionally, RiOT incorporates the text residual connection to mitigate semantic drift by selectively retaining beneficial content across optimization iterations. A tree structure efficiently manages the optimization process, ensuring scalability and flexibility. Extensive experiments across five benchmarks {---} covering commonsense, mathematical, logical, temporal, and semantic reasoning {---} demonstrate that RiOT outperforms both previous prompt optimization methods and manual prompting. Code will be released."
}