  Given a Large Language Model (LLM) generation, how can we identify which
training data led to this generation? In this paper, we proposed RapidIn, a
scalable framework adapting to LLMs for estimating the influence of each
training data. The proposed framework consists of two stages: caching and
retrieval. First, we compress the gradient vectors by over 200,000x, allowing
them to be cached on disk or in GPU/CPU memory. Then, given a generation,
RapidIn efficiently traverses the cached gradients to estimate the influence
within minutes, achieving over a 6,326x speedup. Moreover, RapidIn supports
multi-GPU parallelization to substantially accelerate caching and retrieval.
Our empirical result confirms the efficiency and effectiveness of RapidIn.
