<ul><li>Large Language Models (LLMs) have advanced significantly in recent years due to their large-scale architectures and extensive training on massive datasets.</li><li>Machine unlearning algorithms have been developed to address concerns about data privacy and ownership by removing specific knowledge from models without costly retraining.</li><li>Evaluating the efficacy of unlearning algorithms for LLMs is challenging due to their complexity and generative nature.</li><li>A comprehensive auditing framework has been introduced in this work, including benchmark datasets, unlearning algorithms, and auditing methods to evaluate the effectiveness and robustness of different unlearning strategies.</li></ul>

Does Machine Unlearning Truly Remove Model Knowledge? A Framework for Auditing Unlearning in LLMs

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