@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://aclanthology.org/2025.acl-long.1086/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T RiOT: Efficient Prompt Refinement with Residual Optimization Tree
%A Zhou, Chenyi
%A Shi, Zhengyan
%A Yao, Yuan
%A Liang, Lei
%A Chen, Huajun
%A Zhang, Qiang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhou-etal-2025-riot
%X 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.
%R 10.18653/v1/2025.acl-long.1086
%U https://aclanthology.org/2025.acl-long.1086/
%U https://doi.org/10.18653/v1/2025.acl-long.1086
%P 22307-22323
Markdown (Informal)
[RiOT: Efficient Prompt Refinement with Residual Optimization Tree](https://aclanthology.org/2025.acl-long.1086/) (Zhou et al., ACL 2025)
ACL
- Chenyi Zhou, Zhengyan Shi, Yuan Yao, Lei Liang, Huajun Chen, and Qiang Zhang. 2025. RiOT: Efficient Prompt Refinement with Residual Optimization Tree. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22307–22323, Vienna, Austria. Association for Computational Linguistics.