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    <abstract>Evaluating LLMs with a single prompt has proven unreliable, with small changes leading to significant performance differences. However, generating the prompt variations needed for a more robust multi-prompt evaluation is challenging, limiting its adoption in practice. To address this, we introduce PromptSuite, a framework that enables the automatic generation of various prompts. PromptSuite is flexible – working out of the box on a wide range of tasks and benchmarks. It follows a modular prompt design, allowing controlled perturbations to each component, and is extensible, supporting the addition of new components and perturbation types. Through a series of case studies, we show that PromptSuite provides meaningful variations to support strong evaluation practices. All resources, including the Python API, source code, user-friendly web interface, and demonstration video, are available at: https://eliyahabba.github.io/PromptSuite/.</abstract>
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%0 Conference Proceedings
%T PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation
%A Habba, Eliya
%A Dahan, Noam
%A Lior, Gili
%A Stanovsky, Gabriel
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F habba-etal-2025-promptsuite
%X Evaluating LLMs with a single prompt has proven unreliable, with small changes leading to significant performance differences. However, generating the prompt variations needed for a more robust multi-prompt evaluation is challenging, limiting its adoption in practice. To address this, we introduce PromptSuite, a framework that enables the automatic generation of various prompts. PromptSuite is flexible – working out of the box on a wide range of tasks and benchmarks. It follows a modular prompt design, allowing controlled perturbations to each component, and is extensible, supporting the addition of new components and perturbation types. Through a series of case studies, we show that PromptSuite provides meaningful variations to support strong evaluation practices. All resources, including the Python API, source code, user-friendly web interface, and demonstration video, are available at: https://eliyahabba.github.io/PromptSuite/.
%U https://aclanthology.org/2025.emnlp-demos.19/
%P 254-263

Markdown (Informal)

[PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation](https://aclanthology.org/2025.emnlp-demos.19/) (Habba et al., EMNLP 2025)

• PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation (Habba et al., EMNLP 2025)

ACL

• Eliya Habba, Noam Dahan, Gili Lior, and Gabriel Stanovsky. 2025. PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 254–263, Suzhou, China. Association for Computational Linguistics.