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Shi-Xuan Liu

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#artificial intelligence Preprint Sep 2026

Distilling Diffusion Score Discrepancy for Efficient Training Data Attribution

Training data attribution for diffusion models aims to identify the training samples that influence a generated instance, but existing methods either require costly per-sample gradient computation or query-specific model optimization. Moreover, most methods attribute changes in a proxy loss rather than changes in the a...

Shi-Xuan Liu, Joan Serrà, Kin-Wai Cheuk et al. · 0 citations

#artificial intelligence Preprint Sep 2026

dattri-LLM: A Unified and Efficient Library for Training Data Attribution at LLM Scale

Training data attribution (TDA) estimates the contribution of individual training examples to model outputs. Most scalable TDA methods rely on per-example gradients, whose computation and use at LLM scale pose challenges in efficiency, compatibility, and extensibility. We introduce dattri-LLM, a TDA library that makes...

Shi-Xuan Liu, Tong-Li Zhou, Jun-Wei Deng et al. · 0 citations