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Computer Science > Machine Learning

arXiv:2403.13793 (cs)
[Submitted on 20 Mar 2024 (v1), last revised 5 Apr 2024 (this version, v2)]

Title:Evaluating Frontier Models for Dangerous Capabilities

Authors: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
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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.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2403.13793 [cs.LG]
  (or arXiv:2403.13793v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2403.13793

Submission history

From: Matthew Rahtz [view email]
[v1] Wed, 20 Mar 2024 17:54:26 UTC (838 KB)
[v2] Fri, 5 Apr 2024 12:26:11 UTC (1,042 KB)
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