Designing DSIC Mechanisms for Data Sharing in the Era of Large Language Models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912415927500800 |
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| author | Ayyoubzadeh, Seyed Moein Shahnazari, Kourosh Keshtparvar, Mohammmadali Fazli, MohammadAmin |
| author_facet | Ayyoubzadeh, Seyed Moein Shahnazari, Kourosh Keshtparvar, Mohammmadali Fazli, MohammadAmin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05379 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Designing DSIC Mechanisms for Data Sharing in the Era of Large Language Models Ayyoubzadeh, Seyed Moein Shahnazari, Kourosh Keshtparvar, Mohammmadali Fazli, MohammadAmin Computer Science and Game Theory Artificial Intelligence Computers and Society 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. |
| title | Designing DSIC Mechanisms for Data Sharing in the Era of Large Language Models |
| topic | Computer Science and Game Theory Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2506.05379 |