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Shh‚ don't say that! Domain Certification in LLMs

Cornelius Emde‚ Alasdair Paren‚ Preetham Arvind‚ Maxime Guillaume Kayser‚ Tom Rainforth‚ Thomas Lukasiewicz‚ Philip Torr and Adel Bibi

Abstract

Large language models (LLMs) are often deployed to do constrained tasks, with narrow domains. For example, customer support bots can be built on top of LLMs, relying on their broad language understanding and capabilities to enhance performance. However, these LLMs are adversarially susceptible, potentially generating outputs outside the intended domain. To formalize, assess and mitigate this risk, we introduce domain certification; a guarantee that accurately characterizes the out-of-domain behavior of language models. We then propose a simple yet effective approach dubbed VALID that provides adversarial bounds as a certificate. Finally, we evaluate our method across a diverse set of datasets, demonstrating that it yields meaningful certificates.

Book Title
Proceedings of the 13th International Conference on Learning Representations‚ ICLR 2025‚ Singapore‚ 24–28 April 2025
Year
2025