WMDP Benchmark
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About
WMDP Benchmark is a dataset designed for evaluating hazardous knowledge in large language models (LLMs) related to biosecurity, cybersecurity, and chemical security. It provides a proxy evaluation to assess the risks of malicious use while developing a cutting-edge unlearning method named RMU. This method aims to reduce a model’s hazardous knowledge without significantly impacting its overall language capabilities. The platform focuses on mitigating risks associated with LLMs and includes expert-written questions that expose potential misuse scenarios. The WMDP Benchmark also prioritizes safety interventions and encourages the leveraging of unlearning techniques to ensure that AI systems are not easily repurposed for malicious purposes.
Platform
Task
Features
• focus on biosecurity, cybersecurity, and chemical security
• evaluation of hazardous knowledge
• mitigation strategies using unlearning
• risk measurement for llms
• expert-written dataset
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