Fairshare Data Pricing via Data Valuation for Large Language Models

TL;DR

This paper proposes a fair data pricing mechanism for large language models that promotes sustainable, high-quality data sourcing by aligning incentives between data sellers and buyers, improving market fairness and model performance.

Contribution

It introduces a theoretical framework and a novel fairshare pricing mechanism based on data valuation, enhancing fairness and sustainability in LLM data markets.

Findings

Fairshare increases seller earnings and market participation.

It improves long-term data quality and model performance.

The mechanism aligns incentives for buyers and sellers.

Abstract

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. It aligns incentives by sustaining seller participation and optimizing utility for both buyers and sellers. Theoretically, we show that fairshare yields mutually optimal outcomes: maximizing long-term buyer utility and seller profit while sustaining market participation. Empirically when training open-source LLMs on complex NLP tasks, including math problems, medical diagnosis, and physical reasoning, fairshare boosts seller earnings and ensures a stable supply of high-quality data, while improving buyers' performance-per-dollar and long-term welfare. Our findings offer a concrete path toward fair, transparent, and economically sustainable data markets for LLM.