Designing DSIC Mechanisms for Data Sharing in the Era of Large Language Models

TL;DR

This paper develops a mechanism design framework for incentivizing truthful, high-quality data sharing among providers for large language models, ensuring fairness, budget balance, and robustness against misreporting.

Contribution

It introduces the Q-MIA and MUT mechanisms that incentivize truthful data reporting, balance budgets, and incorporate future utility, advancing data market design for LLM training.

Findings

Mechanisms outperform volume-based baselines in data quality and cost efficiency.

Proposed mechanisms are robust to misreporting and collusion.

The framework supports privacy-preserving, verifiable implementation.

Abstract

Training large language models (LLMs) requires vast amounts of high-quality data from institutions that face legal, privacy, and strategic constraints. Existing data procurement methods often rely on unverifiable trust or ignore heterogeneous provider costs. We introduce a mechanism-design framework for truthful, trust-minimized data sharing that ensures dominant-strategy incentive compatibility (DSIC), individual rationality, and weak budget balance, while rewarding data based on both quality and learning utility. We formalize a model where providers privately know their data cost and quality, and value arises solely from the data's contribution to model performance. Based on this, we propose the Quality-Weighted Marginal-Incentive Auction (Q-MIA), which ranks providers using a virtual cost metric and uses Myerson-style payments to ensure DSIC and budget feasibility. To support settings with limited liquidity or long-term incentives, we introduce the Marginal Utility Token (MUT), which allocates future rights based on marginal contributions. We unify these in Mixed-MIA, a hybrid mechanism balancing upfront payments and deferred rewards. All mechanisms support verifiable, privacy-preserving implementation. Theoretically and empirically, they outperform volume-based and trust-based baselines, eliciting higher-quality data under budget constraints while remaining robust to misreporting and collusion. This establishes a principled foundation for sustainable and fair data markets for future LLMs.