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PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationEMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the System Demonstrations
EditorsIvan Habernal, Peter Schulam, Jorg Tiedemann
PublisherAssociation for Computational Linguistics (ACL)
Pages254-263
Number of pages10
ISBN (Electronic)9798891763340
DOIs
StatePublished - 2025
Event2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2025 - Suzhou, China
Duration: 4 Nov 20259 Nov 2025

Publication series

NameEMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the System Demonstrations

Conference

Conference2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2025
Country/TerritoryChina
CitySuzhou
Period4/11/259/11/25

Bibliographical note

Publisher Copyright:
© 2025 Association for Computational Linguistics.

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