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 language | English |
|---|---|
| Title of host publication | EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the System Demonstrations |
| Editors | Ivan Habernal, Peter Schulam, Jorg Tiedemann |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 254-263 |
| Number of pages | 10 |
| ISBN (Electronic) | 9798891763340 |
| DOIs | |
| State | Published - 2025 |
| Event | 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2025 - Suzhou, China Duration: 4 Nov 2025 → 9 Nov 2025 |
Publication series
| Name | EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the System Demonstrations |
|---|
Conference
| Conference | 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2025 |
|---|---|
| Country/Territory | China |
| City | Suzhou |
| Period | 4/11/25 → 9/11/25 |
Bibliographical note
Publisher Copyright:© 2025 Association for Computational Linguistics.
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