@inproceedings{cui-etal-2025-see,
title = "{SEE}: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization",
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 = "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.1431/",
doi = "10.18653/v1/2025.acl-long.1431",
pages = "29575--29627",
ISBN = "979-8-89176-251-0",
abstract = "Designing optimal prompts for Large Language Models (LLMs) is a complex and resource-intensive task, often requiring substantial human expertise. Existing approaches typically separate the optimization of prompt instructions and in-context learning examples, leading to incohesive, suboptimal results. To overcome this limitation, we propose a novel Cohesive In-Context Prompt Optimization framework that refines both prompt instructions and examples. In our formulation, coherence refers to the degree to which instructions and examples work synergistically to improve task performance{---}emerging as a byproduct of performance-driven optimization. However, formulating such an optimization in the discrete and high-dimensional space of natural language poses significant challenges in both convergence and computational efficiency. To address these issues, we introduce SEE, a scalable and efficient prompt optimization framework that adopts metaheuristic optimization principles and strategically balances exploration and exploitation to enhance optimization performance and achieve efficient convergence. SEE features a quad-phased design that alternates between global traversal (exploration) and local optimization (exploitation) and adaptively chooses LLM operators during the optimization process. We have conducted a comprehensive evaluation across 35 benchmark tasks, and SEE significantly outperforms state-of-the-art baseline methods by a large margin, achieving an average performance gain of **13.94** while reducing computational costs by **58.67{\%}**."
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<abstract>Designing optimal prompts for Large Language Models (LLMs) is a complex and resource-intensive task, often requiring substantial human expertise. Existing approaches typically separate the optimization of prompt instructions and in-context learning examples, leading to incohesive, suboptimal results. To overcome this limitation, we propose a novel Cohesive In-Context Prompt Optimization framework that refines both prompt instructions and examples. In our formulation, coherence refers to the degree to which instructions and examples work synergistically to improve task performance—emerging as a byproduct of performance-driven optimization. However, formulating such an optimization in the discrete and high-dimensional space of natural language poses significant challenges in both convergence and computational efficiency. To address these issues, we introduce SEE, a scalable and efficient prompt optimization framework that adopts metaheuristic optimization principles and strategically balances exploration and exploitation to enhance optimization performance and achieve efficient convergence. SEE features a quad-phased design that alternates between global traversal (exploration) and local optimization (exploitation) and adaptively chooses LLM operators during the optimization process. We have conducted a comprehensive evaluation across 35 benchmark tasks, and SEE significantly outperforms state-of-the-art baseline methods by a large margin, achieving an average performance gain of **13.94** while reducing computational costs by **58.67%**.</abstract>
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%0 Conference Proceedings
%T SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization
%A Cui, Wendi
%A Zhang, Jiaxin
%A Li, Zhuohang
%A Sun, Hao
%A Lopez, Damien
%A Das, Kamalika
%A Malin, Bradley A.
%A Kumar, Sricharan
%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 cui-etal-2025-see
%X Designing optimal prompts for Large Language Models (LLMs) is a complex and resource-intensive task, often requiring substantial human expertise. Existing approaches typically separate the optimization of prompt instructions and in-context learning examples, leading to incohesive, suboptimal results. To overcome this limitation, we propose a novel Cohesive In-Context Prompt Optimization framework that refines both prompt instructions and examples. In our formulation, coherence refers to the degree to which instructions and examples work synergistically to improve task performance—emerging as a byproduct of performance-driven optimization. However, formulating such an optimization in the discrete and high-dimensional space of natural language poses significant challenges in both convergence and computational efficiency. To address these issues, we introduce SEE, a scalable and efficient prompt optimization framework that adopts metaheuristic optimization principles and strategically balances exploration and exploitation to enhance optimization performance and achieve efficient convergence. SEE features a quad-phased design that alternates between global traversal (exploration) and local optimization (exploitation) and adaptively chooses LLM operators during the optimization process. We have conducted a comprehensive evaluation across 35 benchmark tasks, and SEE significantly outperforms state-of-the-art baseline methods by a large margin, achieving an average performance gain of **13.94** while reducing computational costs by **58.67%**.
%R 10.18653/v1/2025.acl-long.1431
%U https://aclanthology.org/2025.acl-long.1431/
%U https://doi.org/10.18653/v1/2025.acl-long.1431
%P 29575-29627
Markdown (Informal)
[SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization](https://aclanthology.org/2025.acl-long.1431/) (Cui et al., ACL 2025)
ACL
- Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, and Sricharan Kumar. 2025. SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29575–29627, Vienna, Austria. Association for Computational Linguistics.