Utility-Aware Data Pricing: Token-Level Quality and Empirical Training Gain for LLMs

Abstract:Traditional ``row-count $\times$ quality coefficient'' approaches fail to capture the nonlinear utility of data for Large Language Model (LLM) capabilities. We present a utility-aware data valuation framework that moves from static accounting toward utility-based pricing. The framework combines token-level information density and data quality, empirical training contribution estimated by influence functions, proxy models, and Data Shapley values, and cryptographic mechanisms based on hash commitments, Merkle trees, and tamper-evident training ledgers. Preliminary evaluation includes a smoke-scale multi-domain study and a synthetic duplication test. Results show that proxy-based empirical gain achieves strong ranking agreement with proxy-defined realized utility and substantially outperforms row-count and token-count baselines in the tested setting, while the prototype provides a controlled sanity check of the valuation pipeline and its sensitivity to duplication-based manipulation.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2604.22893 [cs.LG]

(or arXiv:2604.22893v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2604.22893 Focus to learn more arXiv-issued DOI via DataCite

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