Utility-Aware Data Pricing: Token-Level Quality and Empirical Training Gain for LLMs
📰 ArXiv cs.AI
Learn to value data for LLM training using token-level quality and empirical training gain, improving model capabilities
Action Steps
- Calculate token-level information density using Shannon entropy
- Estimate empirical training gain for each token
- Apply utility-aware data pricing to value data for LLM training
- Evaluate the impact of data valuation on LLM capabilities
- Refine data valuation framework based on experimental results
Who Needs to Know This
Data scientists and AI engineers working on LLMs can benefit from this approach to optimize data valuation and model training
Key Insight
💡 Token-level quality and empirical training gain can be used to dynamically value data for LLM training
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📊 Utility-aware data pricing for LLMs: valuing data beyond row-count & quality coefficients
Key Takeaways
Learn to value data for LLM training using token-level quality and empirical training gain, improving model capabilities
Full Article
Title: Utility-Aware Data Pricing: Token-Level Quality and Empirical Training Gain for LLMs
Abstract:
arXiv:2604.22893v1 Announce Type: cross Abstract: 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 Quali
Abstract:
arXiv:2604.22893v1 Announce Type: cross Abstract: 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 Quali
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