Author

Yuto Nishida

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Preprint Aug 2026

Measuring Task-Agnostic Training Data Influence Across Language Model Pretraining

This work proposes a measure of training data influence that does not require selecting a downstream task or validation set as the attribution target, and defines an example's influence by how much its gradient update reduces the squared distance to the final parameters of a given pretraining run.

Yuto Nishida, Hirokazu Kiyomaru, Yusuke Oda et al. · 0 citations