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Large language models (LLMs) have shown remarkable proficiency in generating text, benefiting from extensive training on vast textual corpora.
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Certified data removal from machine learning models
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Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot · 2022
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Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong · 2023
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Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang · 2023
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Who’s harry potter? approximate unlearning in llms
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OPTQ: Accurate quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2023
SqueezeLLM: Dense-and-sparse quantization
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Composable interventions for language models
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Eight methods to evaluate robust unlearning in llms
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Knowledge unlearning for mitigating privacy risks in language models
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Llama 2: Open foundation and fine-tuned chat models
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A survey of large language models
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On protecting the data privacy of large language models (llms): A survey
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Negative preference optimization: From catastrophic collapse to effective unlearning
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Data-adaptive safety rules for training reward models
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