AI RESEARCH
Utility-Aware Data Pricing: Token-Level Quality and Empirical Training Gain for LLMs
arXiv CS.LG
•
ArXi:2604.22893v1 Announce Type: new Traditional data valuation methods based on ``row-count $\times$ quality coefficient'' paradigms fail to capture the nuanced, nonlinear contributions that data makes to Large Language Model (LLM) capabilities. This paper presents a dynamic data valuation framework that transitions from static accounting to utility-based pricing. Our approach operates on three layers: (1) token-level information density metrics using Shannon entropy and Data Quality Scores; (2) empirical.