Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value

TL;DR

This paper introduces Unlearning Shapley, a scalable data valuation method using machine unlearning and Shapley values, enabling efficient and practical contribution measurement of data in large models without full retraining.

Contribution

It proposes a novel framework that combines machine unlearning with Monte Carlo sampling to estimate data values efficiently, supporting partial data valuation for large models.

Findings

Matches state-of-the-art accuracy in data valuation

Reduces computational costs by orders of magnitude

Strong correlation between estimated and true data impact

Abstract

The proliferation of large models has intensified the need for efficient data valuation methods to quantify the contribution of individual data providers. Traditional approaches, such as game-theory-based Shapley value and influence-function-based techniques, face prohibitive computational costs or require access to full data and model training details, making them hardly achieve partial data valuation. To address this, we propose Unlearning Shapley, a novel framework that leverages machine unlearning to estimate data values efficiently. By unlearning target data from a pretrained model and measuring performance shifts on a reachable test set, our method computes Shapley values via Monte Carlo sampling, avoiding retraining and eliminating dependence on full data. Crucially, Unlearning Shapley supports both full and partial data valuation, making it scalable for large models (e.g., LLMs) and practical for data markets. Experiments on benchmark datasets and large-scale text corpora demonstrate that our approach matches the accuracy of state-of-the-art methods while reducing computational overhead by orders of magnitude. Further analysis confirms a strong correlation between estimated values and the true impact of data subsets, validating its reliability in real-world scenarios. This work bridges the gap between data valuation theory and practical deployment, offering a scalable, privacy-compliant solution for modern AI ecosystems.