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Large language models are finetuned to refuse questions about hazardous knowledge, but these protections can often be bypassed.
Hila Gonen and Yoav Goldberg · 1903
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Robust locally weighted regression and smoothing scatterplots
William S Cleveland · 1979
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Pointer sentinel mixture models, 2016
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Interpreting gpt: the logit lens, 2020
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Machine unlearning
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Data poisoning won’t save you from facial recognition
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How many layers and why? An analysis of the model depth in transformers
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Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, et al · 2022
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Knowledge unlearning for mitigating privacy risks in language models
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Jiaao Chen and Diyi Yang · 2023
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Who’s harry potter? approximate unlearning in llms, 2023
Ronen Eldan and Mark Russinovich · 2023
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Catastrophic jailbreak of open-source llms via exploiting generation
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Copyright violations and large language models, 2023
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Split, unlearn, merge: Leveraging data attributes for more effective unlearning in llms
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Universal and transferable adversarial attacks on aligned language models
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Foundational challenges in assuring alignment and safety of large language models
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Machine unlearning fails to remove data poisoning attacks
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Fast adversarial attacks on language models in one gpu minute
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