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arXiv:2403.13793cs.LG

Evaluating Frontier Models for Dangerous Capabilities

Mary Phuong, Matthew Aitchison, Elliot Catt, Sarah Cogan, Alexandre Kaskasoli, Victoria Krakovna, David Lindner, Matthew Rahtz, Yannis Assael, Sarah Hodkinson, Heidi Howard, Tom Lieberum, Ramana Kumar, Maria Abi Raad, Albert Webson, Lewis Ho, Sharon Lin, Sebastian Farquhar, Marcus Hutter, Gregoire Deletang, Anian Ruoss, Seliem El-Sayed, Sasha Brown, Anca Dragan, Rohin Shah, Allan Dafoe, Toby Shevlane

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

To understand the risks posed by a new AI system, we must understand what it can and cannot do. Building on prior work, we introduce a programme of new "dangerous capability" evaluations and pilot them on Gemini 1.0 models. Our evaluations cover four areas: (1) persuasion and deception; (2) cyber-security; (3) self-proliferation; and (4) self-reasoning. We do not find evidence of strong dangerous capabilities in the models we evaluated, but we flag early warning signs. Our goal is to help advance a rigorous science of dangerous capability evaluation, in preparation for future models.

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cs.LGMachine Learning

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Jun 2024 to Jun 2024. Found in 1 HN submission from Jun 2024 to Jun 2024. Open submission discussions.

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    • To quote Ludovic Peran, our amazing safety lead: Literature has identified self-proliferation as dangerous capability of models, and details about how to define it and example of form it can take have been openly discussed by GDM (https://arxiv.org/pdf/2403.13793). Current Gemma…

      alekandreevView on HNParent context

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