CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept
Keywords: machine unlearning, discrete representation, AI safety, LLM
TL;DR: Zero-Shot Unlearning of Large Language Models Using Discrete Representations
Abstract: Language Models (LMs) offer extensive knowledge across various domains, but they may inadvertently memorize sensitive, unauthorized, or malicious data, such as personal information in the medical and financial sectors. Machine unlearning methods aim to remove specific information from models after training to address this. However, current approaches require additional model training or struggle to effectively erase particular data points and their associated context due to LMs' complex, dense, and continuous nature. In this study, we propose a novel amortized unlearning approach using codebook features and Sparse Autoencoders (SAEs). By leveraging a bottleneck to decompose the activation space and regulate information flow, our method efficiently unlearns targeted information while preserving the model's performance on unrelated data. To the best of our knowledge, this is the first work that successfully enables unlearning specific topics with contextual relevance in an LM, marking a significant step towards real-world applications of machine unlearning.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 6173
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