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.

翻译：大语言模型（LLMs）的持续改进日益依赖于获取高质量、用户生成的数据，然而此类数据不仅提供成本高昂，且常因隐私和努力考量而被保留。这构成了一个根本性的设计挑战：当模型改进需要协调的、达到阈值水平的输入，而贡献仍是私人成本且部分可逆时，如何激励用户贡献数据？我们开发并从理论上分析了针对用户数据贡献的激励机制，明确考虑了阈值效应和可逆性，重点研究了如何联合设计补贴与撤回权以克服协调失灵。作为自然基准，我们首先考虑基于补贴的激励，在此机制下，用户以私下最优的最低贡献回应发布的付款。这些分散的回应可能低于改进阈值，导致补贴支出却无模型改进。随后，我们分析了带有撤回权的机制，其中用户报告成本，提供者集中分配贡献负担，用户可在训练前撤回。我们证明，将成本报告与个性化分配相结合，可通过确保仅在改进可持续时收集数据，从而消除低效供给，将不可行的情况转化为零结果而非补贴泄漏。最后，我们比较了两种撤回协议。同步协议可实现更低的总成本，而小优先顺序协议则更好地激励参与，鼓励更多的数据提供，从而提高跨越改进阈值的概率。