@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T A Systematic Survey of Automatic Prompt Optimization Techniques
%A Ramnath, Kiran
%A Zhou, Kang
%A Guan, Sheng
%A Mishra, Soumya Smruti
%A Qi, Xuan
%A Shen, Zhengyuan
%A Wang, Shuai
%A Woo, Sangmin
%A Jeoung, Sullam
%A Wang, Yawei
%A Wang, Haozhu
%A Ding, Han
%A Lu, Yuzhe
%A Xu, Zhichao
%A Zhou, Yun
%A Srinivasan, Balasubramaniam
%A Yan, Qiaojing
%A Chen, Yueyan
%A Ding, Haibo
%A Xu, Panpan
%A Cheong, Lin Lee
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ramnath-etal-2025-systematic
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1681/
%P 33066-33098
Markdown (Informal)
[A Systematic Survey of Automatic Prompt Optimization Techniques](https://aclanthology.org/2025.emnlp-main.1681/) (Ramnath et al., EMNLP 2025)
ACL
- Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, and Lin Lee Cheong. 2025. A Systematic Survey of Automatic Prompt Optimization Techniques. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 33066–33098, Suzhou, China. Association for Computational Linguistics.