Incentivizing User Data Contributions for LLM Improvement under Withdrawal Rights

2026-05-08

AI Summary

This study addresses the challenge of incentivizing sufficient high-quality data contributions from users in a setting where users retain the right to withdraw their data and face privately known contribution costs, with the goal of surpassing the threshold required for large language model improvement. The work proposes a novel cost-reporting-based centralized task allocation mechanism that integrates data withdrawal rights with threshold-driven model enhancement, coordinating user decisions through subsidies and personalized assignments. The mechanism transforms infeasible scenarios into zero-expenditure outcomes rather than inefficient subsidies and compares two withdrawal protocols: synchronous and small-first sequential. Theoretical analysis demonstrates that the proposed mechanism eliminates inefficient supply; while the synchronous protocol incurs lower total costs, the small-first sequential protocol achieves higher participation rates and a greater probability of crossing the performance threshold.

Abstract

The continued improvement of large language models (LLMs) increasingly depends on eliciting high-quality, user-generated data, yet such data are costly to provide and often withheld due to privacy and effort concerns. This creates a fundamental design challenge: how to incentivize data contribution when model improvements require coordinated, threshold-level inputs, while contributions remain privately costly and partially reversible. We develop and theoretically analyze incentive mechanisms for user data contribution that explicitly account for threshold effects and reversibility, focusing on how subsidies and withdrawal rights can be jointly designed to overcome coordination failure. As a natural benchmark, we first consider subsidy-based incentives, under which users respond to posted payments with privately optimal floor contributions. These decentralized responses may fall below the improvement threshold, resulting in subsidy expenditure without model improvements. We then analyze mechanisms with withdrawal rights, in which users report costs, the provider centrally assigns contribution burdens, and users may withdraw before training. We prove that combining cost reporting with personalized assignment can eliminate inefficient provision by ensuring that data are collected only when improvement is sustainable, converting infeasible instances into a null outcome rather than subsidy leakage. Finally, we compare two withdrawal protocols. The simultaneous protocol can achieve lower total cost, while the small-first sequential protocol better incentivizes participation, encouraging greater data provision and thereby increasing the probability of crossing the improvement threshold.

Problem

• incentive mechanism
• user data contribution
• withdrawal rights
• threshold effect
• large language models

Innovation

• incentive mechanisms
• withdrawal rights
• threshold effects
• cost reporting
• coordination failure