Learning diverse attacks on large language models for robust red-teaming and safety tuning

Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typi...

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Bibliographische Detailangaben

Hauptverfasser:	Lee, Seanie, Kim, Minsu, Cherif, Lynn, Dobre, David, Lee, Juho, Hwang, Sung Ju, Kawaguchi, Kenji, Gidel, Gauthier, Bengio, Yoshua, Malkin, Nikolay, Jain, Moksh
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computation and Language Computer Science - Cryptography and Security Computer Science - Learning
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