Token-wise Influential Training Data Retrieval for Large Language Models
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 200,000 x,
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 …
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 200,000 x,
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 …
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.
arxiv.org