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This paper explores Machine Unlearning (MU), an emerging field that is gaining increased attention due to concerns about neural models unintentionally remembering personal or sensitive information.
Hellaswag: Can a machine really finish your sentence?
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Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
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Deep Unlearning via Randomized Conditionally Independent Hessians
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Locating and editing factual knowledge in gpt
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Memorization without overfitting: Analyzing the training dynamics of large language models
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Machine unlearning
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A.; Achille, A.; and Soatto, S. 2020a
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Jang, J.; Yoon, D.; Yang, S.; Cha, S.; Lee, M.; Logeswaran, L.; and Seo, M. 2023 · 2023
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Deductive Verification of Chain-of-Thought Reasoning
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Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
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Reflexion: Language agents with verbal reinforcement learning
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KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
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Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills
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