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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.03218 |
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| _version_ | 1866911877827657728 |
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| author | Li, Nathaniel Pan, Alexander Gopal, Anjali Yue, Summer Berrios, Daniel Gatti, Alice Li, Justin D. Dombrowski, Ann-Kathrin Goel, Shashwat Phan, Long Mukobi, Gabriel Helm-Burger, Nathan Lababidi, Rassin Justen, Lennart Liu, Andrew B. Chen, Michael Barrass, Isabelle Zhang, Oliver Zhu, Xiaoyuan Tamirisa, Rishub Bharathi, Bhrugu Khoja, Adam Zhao, Zhenqi Herbert-Voss, Ariel Breuer, Cort B. Marks, Samuel Patel, Oam Zou, Andy Mazeika, Mantas Wang, Zifan Oswal, Palash Lin, Weiran Hunt, Adam A. Tienken-Harder, Justin Shih, Kevin Y. Talley, Kemper Guan, John Kaplan, Russell Steneker, Ian Campbell, David Jokubaitis, Brad Levinson, Alex Wang, Jean Qian, William Karmakar, Kallol Krishna Basart, Steven Fitz, Stephen Levine, Mindy Kumaraguru, Ponnurangam Tupakula, Uday Varadharajan, Vijay Wang, Ruoyu Shoshitaishvili, Yan Ba, Jimmy Esvelt, Kevin M. Wang, Alexandr Hendrycks, Dan |
| author_facet | Li, Nathaniel Pan, Alexander Gopal, Anjali Yue, Summer Berrios, Daniel Gatti, Alice Li, Justin D. Dombrowski, Ann-Kathrin Goel, Shashwat Phan, Long Mukobi, Gabriel Helm-Burger, Nathan Lababidi, Rassin Justen, Lennart Liu, Andrew B. Chen, Michael Barrass, Isabelle Zhang, Oliver Zhu, Xiaoyuan Tamirisa, Rishub Bharathi, Bhrugu Khoja, Adam Zhao, Zhenqi Herbert-Voss, Ariel Breuer, Cort B. Marks, Samuel Patel, Oam Zou, Andy Mazeika, Mantas Wang, Zifan Oswal, Palash Lin, Weiran Hunt, Adam A. Tienken-Harder, Justin Shih, Kevin Y. Talley, Kemper Guan, John Kaplan, Russell Steneker, Ian Campbell, David Jokubaitis, Brad Levinson, Alex Wang, Jean Qian, William Karmakar, Kallol Krishna Basart, Steven Fitz, Stephen Levine, Mindy Kumaraguru, Ponnurangam Tupakula, Uday Varadharajan, Vijay Wang, Ruoyu Shoshitaishvili, Yan Ba, Jimmy Esvelt, Kevin M. Wang, Alexandr Hendrycks, Dan |
| contents | 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 3,668 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 RMU, a state-of-the-art unlearning method based on controlling model representations. RMU 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03218 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning Li, Nathaniel Pan, Alexander Gopal, Anjali Yue, Summer Berrios, Daniel Gatti, Alice Li, Justin D. Dombrowski, Ann-Kathrin Goel, Shashwat Phan, Long Mukobi, Gabriel Helm-Burger, Nathan Lababidi, Rassin Justen, Lennart Liu, Andrew B. Chen, Michael Barrass, Isabelle Zhang, Oliver Zhu, Xiaoyuan Tamirisa, Rishub Bharathi, Bhrugu Khoja, Adam Zhao, Zhenqi Herbert-Voss, Ariel Breuer, Cort B. Marks, Samuel Patel, Oam Zou, Andy Mazeika, Mantas Wang, Zifan Oswal, Palash Lin, Weiran Hunt, Adam A. Tienken-Harder, Justin Shih, Kevin Y. Talley, Kemper Guan, John Kaplan, Russell Steneker, Ian Campbell, David Jokubaitis, Brad Levinson, Alex Wang, Jean Qian, William Karmakar, Kallol Krishna Basart, Steven Fitz, Stephen Levine, Mindy Kumaraguru, Ponnurangam Tupakula, Uday Varadharajan, Vijay Wang, Ruoyu Shoshitaishvili, Yan Ba, Jimmy Esvelt, Kevin M. Wang, Alexandr Hendrycks, Dan Machine Learning Artificial Intelligence Computation and Language Computers and Society 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 3,668 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 RMU, a state-of-the-art unlearning method based on controlling model representations. RMU 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 |
| title | The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning |
| topic | Machine Learning Artificial Intelligence Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2403.03218 |