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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.16923 |
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| _version_ | 1866909972853424128 |
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| author | Ramnath, Kiran Zhou, Kang Guan, Sheng Mishra, Soumya Smruti Qi, Xuan Shen, Zhengyuan Wang, Shuai Woo, Sangmin Jeoung, Sullam Wang, Yawei Wang, Haozhu Ding, Han Lu, Yuzhe Xu, Zhichao Zhou, Yun Srinivasan, Balasubramaniam Yan, Qiaojing Chen, Yueyan Ding, Haibo Xu, Panpan Cheong, Lin Lee |
| author_facet | Ramnath, Kiran Zhou, Kang Guan, Sheng Mishra, Soumya Smruti Qi, Xuan Shen, Zhengyuan Wang, Shuai Woo, Sangmin Jeoung, Sullam Wang, Yawei Wang, Haozhu Ding, Han Lu, Yuzhe Xu, Zhichao Zhou, Yun Srinivasan, Balasubramaniam Yan, Qiaojing Chen, Yueyan Ding, Haibo Xu, Panpan Cheong, Lin Lee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Systematic Survey of Automatic Prompt Optimization Techniques Ramnath, Kiran Zhou, Kang Guan, Sheng Mishra, Soumya Smruti Qi, Xuan Shen, Zhengyuan Wang, Shuai Woo, Sangmin Jeoung, Sullam Wang, Yawei Wang, Haozhu Ding, Han Lu, Yuzhe Xu, Zhichao Zhou, Yun Srinivasan, Balasubramaniam Yan, Qiaojing Chen, Yueyan Ding, Haibo Xu, Panpan Cheong, Lin Lee Computation and Language Artificial Intelligence 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. |
| title | A Systematic Survey of Automatic Prompt Optimization Techniques |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.16923 |