RapidGrad is a key component of the RapidIn framework, designed to solve the efficiency bottleneck of estimating the influence of training data on Large Language Model (LLM) generations. It represents a highly compressed, low-dimensional version of a gradient vector, reducing data size from billions of parameters to megabytes or even kilobytes. Towards AI +2 Core Mechanism of RapidGrad RapidGrad is created by projecting high-dimensional gradients into a lower-dimensional space using a process called
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