Token-wise Influential Training Data Retrieval for Large Language Models

• URL: http://arxiv.org/abs/2405.11724v2
• Date: Tue, 22 Oct 2024 19:07:08 GMT
• Title: Token-wise Influential Training Data Retrieval for Large Language Models
• Authors: Huawei Lin, Jikai Long, Zhaozhuo Xu, Weijie Zhao,
• Abstract summary: RapidIn is a framework adapting to Large Language Models for estimating the influence of each training data. RapidIn efficiently traverses the cached gradients to estimate the influence within minutes, achieving over a 6,326x speedup.
• Score: 8.42342318438945
• License:
• Abstract: 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.

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