@inproceedings{zheng-etal-2025-greaterprompt,
title = "{G}reater{P}rompt: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization",
author = "Zheng, Wenliang and
Das, Sarkar Snigdha Sarathi and
Zhang, Yusen and
Zhang, Rui",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.39/",
doi = "10.18653/v1/2025.acl-demo.39",
pages = "405--415",
ISBN = "979-8-89176-253-4",
abstract = "LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significantly dependent on the quality and structure of the input prompts, making prompt design a critical factor for their performance. Recent advancements in automated prompt optimization have introduced diverse techniques that automatically enhance prompts to better align model outputs with user expectations. However, these methods often suffer from the lack of standardization and compatibility across different techniques, limited flexibility in customization, inconsistent performance across model scales, and they often exclusively rely on expensive proprietary LLM APIs. To fill in this gap, we introduce GreaterPrompt, a novel framework that democratizes prompt optimization by unifying diverse methods under a unified, customizable API while delivering highly effective prompts for different tasks. Our framework flexibly accommodates various model scales by leveraging both text feedback-based optimization for larger LLMs and internal gradient-based optimization for smaller models to achieve powerful and precise prompt improvements. Moreover, we provide a user-friendly Web UI that ensures accessibility for non-expert users, enabling broader adoption and enhanced performance across various user groups and application scenarios. GreaterPrompt is available at https://github.com/psunlpgroup/GreaterPrompt via GitHub, PyPI, and web user interfaces."
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<abstract>LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significantly dependent on the quality and structure of the input prompts, making prompt design a critical factor for their performance. Recent advancements in automated prompt optimization have introduced diverse techniques that automatically enhance prompts to better align model outputs with user expectations. However, these methods often suffer from the lack of standardization and compatibility across different techniques, limited flexibility in customization, inconsistent performance across model scales, and they often exclusively rely on expensive proprietary LLM APIs. To fill in this gap, we introduce GreaterPrompt, a novel framework that democratizes prompt optimization by unifying diverse methods under a unified, customizable API while delivering highly effective prompts for different tasks. Our framework flexibly accommodates various model scales by leveraging both text feedback-based optimization for larger LLMs and internal gradient-based optimization for smaller models to achieve powerful and precise prompt improvements. Moreover, we provide a user-friendly Web UI that ensures accessibility for non-expert users, enabling broader adoption and enhanced performance across various user groups and application scenarios. GreaterPrompt is available at https://github.com/psunlpgroup/GreaterPrompt via GitHub, PyPI, and web user interfaces.</abstract>
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%0 Conference Proceedings
%T GreaterPrompt: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization
%A Zheng, Wenliang
%A Das, Sarkar Snigdha Sarathi
%A Zhang, Yusen
%A Zhang, Rui
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F zheng-etal-2025-greaterprompt
%X LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significantly dependent on the quality and structure of the input prompts, making prompt design a critical factor for their performance. Recent advancements in automated prompt optimization have introduced diverse techniques that automatically enhance prompts to better align model outputs with user expectations. However, these methods often suffer from the lack of standardization and compatibility across different techniques, limited flexibility in customization, inconsistent performance across model scales, and they often exclusively rely on expensive proprietary LLM APIs. To fill in this gap, we introduce GreaterPrompt, a novel framework that democratizes prompt optimization by unifying diverse methods under a unified, customizable API while delivering highly effective prompts for different tasks. Our framework flexibly accommodates various model scales by leveraging both text feedback-based optimization for larger LLMs and internal gradient-based optimization for smaller models to achieve powerful and precise prompt improvements. Moreover, we provide a user-friendly Web UI that ensures accessibility for non-expert users, enabling broader adoption and enhanced performance across various user groups and application scenarios. GreaterPrompt is available at https://github.com/psunlpgroup/GreaterPrompt via GitHub, PyPI, and web user interfaces.
%R 10.18653/v1/2025.acl-demo.39
%U https://aclanthology.org/2025.acl-demo.39/
%U https://doi.org/10.18653/v1/2025.acl-demo.39
%P 405-415
Markdown (Informal)
[GreaterPrompt: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization](https://aclanthology.org/2025.acl-demo.39/) (Zheng et al., ACL 2025)
ACL