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Estimating Training Data Influence by Tracing Gradient Descent
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Estimating Training Data Influence by Tracing Gradient Descent
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We introduce a method called TracIn that computes the influence of a training example on a prediction made by the model. The idea is to trace how the loss on the test point changes during the training process whenever the training example of interest was utilized. We provide a scalable implementation of TracIn via: (a) a first-order gradient approximation to the exact computation, (b) saved checkpoints of standard training procedures, and (c) cherry-picking layers of a deep neural network. In contrast with previously proposed methods, TracIn is simple to implement; all it needs is the ability to work with gradients, checkpoints, and loss functions. The method is general. It applies to any machine learning model trained using stochastic gradient descent or a variant of it, agnostic of architecture, domain and task. We expect the method to be widely useful within processes that study and improve training data.
Forward citations
Cited by 9 Pith papers
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Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation
RISE applies CountSketch to dual lexical and semantic channels derived from output-layer gradient outer products, cutting data attribution storage by up to 112x and enabling retrospective and prospective influence ana...
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Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation
Influence scoring can use only forward passes: CountSketch-compressed outer products of the LM-head residual and final hidden state give accurate attribution and valuation from 14M to 32B parameters.
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GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
GUDA approximates leave-one-group-out counterfactual models with unlearning and ranks group influence by ELBO differences.
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DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks
DUET is a global-to-local method that optimizes LLM training data mixtures via Bayesian optimization guided by influence-based selection and feedback from unseen evaluation tasks, with a regret bound showing convergen...
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Dataset Distillation by Influence Matching
Inf-Match distills datasets by matching estimated parameter influence of real and synthetic data, reporting SOTA classification and retrieval, but with an unsupported theoretical core.
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When unlearning is free: leveraging low influence points to reduce computational costs
Low-influence training points can be dropped from forget/retain sets before unlearning, cutting runtime up to ~50% with little measured loss in accuracy or MIA-based privacy.
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