Fairshare Data Pricing via Data Valuation for Large Language Models

Understand

Training data is the backbone of large language models (LLMs), yet today's data markets often operate under exploitative pricing -- sourcing data from marginalized groups with little pay or recognition.

• This paper introduces a theoretical framework for LLM data markets, modeling the strategic interactions between buyers (LLM builders) and sellers (human annotators).
• We begin with theoretical and empirical analysis showing how exploitative pricing drives high-quality sellers out of the market, degrading data quality and long-term model performance.
• Then we introduce fairshare, a pricing mechanism grounded in data valuation that quantifies each data's contribution.