1 Trillion Token (1TT) Platform: A Novel Framework for Efficient Data Sharing and Compensation in Large Language Models
This addresses the challenge of data scarcity and compensation for data contributors in the LLM field, though it appears incremental as it builds on existing data-sharing concepts with a new framework.
The paper tackles the problem of incentivizing large-scale data sharing for LLMs by proposing the 1 Trillion Token Platform, which enables data contributors to share non-disclosed datasets and receive monetary compensation from data consumers based on predefined profit-sharing arrangements, fostering collaboration to advance NLP and LLM technologies.
In this paper, we propose the 1 Trillion Token Platform (1TT Platform), a novel framework designed to facilitate efficient data sharing with a transparent and equitable profit-sharing mechanism. The platform fosters collaboration between data contributors, who provide otherwise non-disclosed datasets, and a data consumer, who utilizes these datasets to enhance their own services. Data contributors are compensated in monetary terms, receiving a share of the revenue generated by the services of the data consumer. The data consumer is committed to sharing a portion of the revenue with contributors, according to predefined profit-sharing arrangements. By incorporating a transparent profit-sharing paradigm to incentivize large-scale data sharing, the 1TT Platform creates a collaborative environment to drive the advancement of NLP and LLM technologies.