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Online Adaptation of Language Models with a Memory of Amortized Contexts
Neural Information Processing Systems
However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention.
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A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
Neural Information Processing Systems
Privacy protection of users' entire contribution of samples is important in distributed
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