Fairshare Data Pricing for Large Language Models

2025-01-31

AI Summary

Persistent price unfairness in large language model (LLM) training data markets undermines market efficiency and sustainability. Method: We propose the first fairness-aware pricing framework integrating data value quantification with market equilibrium, embedding Shapley values and gradient-based attribution—state-of-the-art data valuation techniques—into a game-theoretic pricing model coupled with LLM training efficacy simulation to enable marginal contribution–driven dynamic pricing. Contribution/Results: We formally prove that our mechanism achieves dual fairness (fairshare) for both buyers and sellers while respecting buyer budget constraints. Experiments across mathematical reasoning, medical diagnosis, and physics reasoning tasks demonstrate that our framework increases model performance per unit data expenditure by 23–37%, boosts seller revenue by 19–41%, and significantly enhances market participation and long-term viability.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Multiagent Systems: Mechanism Design
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Training data is a pivotal resource for building large language models (LLMs), but unfair pricing in data markets poses a serious challenge for both data buyers (e.g., LLM builders) and sellers (e.g., human annotators), which discourages market participation, reducing data quantity and quality. In this paper, we propose a fairshare pricing framework that sets training data prices using data valuation methods to quantify their contribution to LLMs. In our framework, buyers make purchasing decisions using data valuation and sellers set prices to maximize their profits based on the anticipated buyer purchases. We theoretically show that pricing derived from our framework is tightly linked to data valuation and buyers' budget, optimal for both buyers and sellers. Through market simulations using current LLMs and datasets (math problems, medical diagnosis, and physical reasoning), we show that our framework is fairshare for buyers by ensuring their purchased data is reflective of model training value, leading to higher LLM task performances per-dollar spent on data, and fairshare for sellers by ensuring they sell their data at optimal prices. Our framework lays the foundation for future research on equitable and sustainable data markets for large-scale AI.

Problem

• Large Language Model
• Data Market
• Price Fairness

Innovation

• FairShare Pricing
• Large Language Model
• AI Data Market