@inproceedings{wu-etal-2024-strago,
title = "{S}tra{G}o: Harnessing Strategic Guidance for Prompt Optimization",
author = "Wu, Yurong and
Gao, Yan and
Zhu, Bin and
Zhou, Zineng and
Sun, Xiaodi and
Yang, Sheng and
Lou, Jian-Guang and
Ding, Zhiming and
Yang, Linjun",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.588",
pages = "10043--10061",
abstract = "Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt effectiveness, they often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures. Furthermore, these methods tend to rely heavily on LLMs{'} intrinsic capabilities for prompt optimization tasks. In this paper, we introduce STRAGO (StrategicGuided Optimization), a novel approach designed to mitigate prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. STRAGO employs a how-to-do methodology, integrating in-context learning to formulate specific, actionable strategies that provide detailed, step-by-step guidance for prompt optimization. Extensive experiments conducted across a range of tasks, including reasoning, natural language understanding, domain-specific knowledge, and industrial applications, demonstrate STRAGO{'}s superior performance. It establishes a new stateof-the-art in prompt optimization, showcasing its ability to deliver stable and effective prompt improvements.",
}
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<abstract>Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt effectiveness, they often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures. Furthermore, these methods tend to rely heavily on LLMs’ intrinsic capabilities for prompt optimization tasks. In this paper, we introduce STRAGO (StrategicGuided Optimization), a novel approach designed to mitigate prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. STRAGO employs a how-to-do methodology, integrating in-context learning to formulate specific, actionable strategies that provide detailed, step-by-step guidance for prompt optimization. Extensive experiments conducted across a range of tasks, including reasoning, natural language understanding, domain-specific knowledge, and industrial applications, demonstrate STRAGO’s superior performance. It establishes a new stateof-the-art in prompt optimization, showcasing its ability to deliver stable and effective prompt improvements.</abstract>
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%0 Conference Proceedings
%T StraGo: Harnessing Strategic Guidance for Prompt Optimization
%A Wu, Yurong
%A Gao, Yan
%A Zhu, Bin
%A Zhou, Zineng
%A Sun, Xiaodi
%A Yang, Sheng
%A Lou, Jian-Guang
%A Ding, Zhiming
%A Yang, Linjun
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wu-etal-2024-strago
%X Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt effectiveness, they often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures. Furthermore, these methods tend to rely heavily on LLMs’ intrinsic capabilities for prompt optimization tasks. In this paper, we introduce STRAGO (StrategicGuided Optimization), a novel approach designed to mitigate prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. STRAGO employs a how-to-do methodology, integrating in-context learning to formulate specific, actionable strategies that provide detailed, step-by-step guidance for prompt optimization. Extensive experiments conducted across a range of tasks, including reasoning, natural language understanding, domain-specific knowledge, and industrial applications, demonstrate STRAGO’s superior performance. It establishes a new stateof-the-art in prompt optimization, showcasing its ability to deliver stable and effective prompt improvements.
%U https://aclanthology.org/2024.findings-emnlp.588
%P 10043-10061
Markdown (Informal)
[StraGo: Harnessing Strategic Guidance for Prompt Optimization](https://aclanthology.org/2024.findings-emnlp.588) (Wu et al., Findings 2024)
ACL
- Yurong Wu, Yan Gao, Bin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, and Linjun Yang. 2024. StraGo: Harnessing Strategic Guidance for Prompt Optimization. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10043–10061, Miami, Florida, USA. Association for Computational Linguistics.