# Do Data Valuations Make Good Data Prices?

arXiv: https://arxiv.org/abs/2504.05563 · HTML: https://mlsanity.com/paper/2504.05563/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Estimating Training Data Influence by Tracing Gradient Descent](https://mlsanity.com/paper/2002.08484/) (2020)
- [Will we run out of data? Limits of LLM scaling based on human-generated data](https://mlsanity.com/paper/2211.04325/) (2022)
- [Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance](https://mlsanity.com/paper/2403.16952/) (2024)
- [Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms](https://mlsanity.com/paper/1908.08619/) (2019)
- [A Question-Entailment Approach to Question Answering](https://mlsanity.com/paper/1901.08079/) (2019)
- [MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models](https://mlsanity.com/paper/2406.06046/) (2024)
- [Data Shapley in One Training Run](https://mlsanity.com/paper/2406.11011/) (2024)
- [Consent in Crisis: The Rapid Decline of the AI Data Commons](https://mlsanity.com/paper/2407.14933/) (2024)
- [Efficient Two-Sided Markets with Limited Information](https://mlsanity.com/paper/2003.07503/) (2020)
- [Towards Data Auctions with Externalities](https://mlsanity.com/paper/2003.08345/) (2020)
- [A Survey on Data Markets](https://mlsanity.com/paper/2411.07267/) (2024)
- [Data Sharing Markets](https://mlsanity.com/paper/2107.08630/) (2021)

## Built on by (most cited first, 2 of 2)

- [Can Performant LLMs Be Ethical? Quantifying the Impact of Web Crawling Opt-Outs](https://mlsanity.com/paper/2504.06219/) (2025)
- [Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval](https://mlsanity.com/paper/2610.02070/) (2026)

## Abstract (arXiv)

As large language models increasingly rely on external data sources, compensating data contributors has become a central concern. But how should these payments be devised? We revisit data valuations from a textitmarket-design perspective where payments serve to compensate data owners for the textitprivate heterogeneous costs they incur for collecting and sharing data. We show that popular valuation methods-such as Leave-One-Out and Data Shapley-make for poor payments. They fail to ensure truthful reporting of the costs, leading to textitinefficient market outcomes. To address this, we adapt well-established payment rules from mechanism design, namely Myerson and Vickrey-Clarke-Groves (VCG), to the data market setting. We show that Myerson payment is the minimal truthful mechanism, optimal from the buyer's perspective. Additionally, we identify a condition under which both data buyers and sellers are utility-satisfied, and the market achieves efficiency. Our findings highlight the importance of incorporating incentive compatibility into data valuation design, paving the way for more robust and efficient data markets. Our data market framework is readily applicable to real-world scenarios. We illustrate this with simulations of contributor compensation in an LLM based retrieval-augmented generation (RAG) marketplace tasked with challenging medical question answering.