This research introduces Fairshare, a novel pricing framework that quantifies the contribution of training data to Large Language Models, ensuring fair compensation for data sellers while optimizing value for buyers.

• Establishes data prices based on measurable contributions to model performance
• Creates a more sustainable data marketplace by incentivizing quality data creation
• Demonstrates practical implementation with various datasets including medical diagnosis scenarios
• Addresses a critical market failure in current LLM development ecosystems

For the medical sector, this framework enables more equitable pricing of valuable clinical data, potentially increasing the availability of high-quality medical datasets for specialized healthcare LLMs while ensuring healthcare providers are fairly compensated for their contributions.

Fairshare Data Pricing for Large Language Models