The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
arxiv(2024)
摘要
The White House Executive Order on Artificial Intelligence highlights the
risks of large language models (LLMs) empowering malicious actors in developing
biological, cyber, and chemical weapons. To measure these risks of malicious
use, government institutions and major AI labs are developing evaluations for
hazardous capabilities in LLMs. However, current evaluations are private,
preventing further research into mitigating risk. Furthermore, they focus on
only a few, highly specific pathways for malicious use. To fill these gaps, we
publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a
dataset of 4,157 multiple-choice questions that serve as a proxy measurement of
hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP
was developed by a consortium of academics and technical consultants, and was
stringently filtered to eliminate sensitive information prior to public
release. WMDP serves two roles: first, as an evaluation for hazardous knowledge
in LLMs, and second, as a benchmark for unlearning methods to remove such
hazardous knowledge. To guide progress on unlearning, we develop CUT, a
state-of-the-art unlearning method based on controlling model representations.
CUT reduces model performance on WMDP while maintaining general capabilities in
areas such as biology and computer science, suggesting that unlearning may be a
concrete path towards reducing malicious use from LLMs. We release our
benchmark and code publicly at https://wmdp.ai
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