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