# 1 Trillion Token (1TT) Platform: A Novel Framework for Efficient Data Sharing and Compensation in Large Language Models

arXiv: https://arxiv.org/abs/2409.20149 · HTML: https://mlsanity.com/paper/2409.20149/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Large Language Models as Data Preprocessors](https://mlsanity.com/paper/2308.16361/) (2023)
- [DQI: Measuring Data Quality in NLP](https://mlsanity.com/paper/2005.00816/) (2020)
- [A Survey on Optical Character Recognition System](https://mlsanity.com/paper/1710.05703/) (2017)
- [Insights from an experiment crowdsourcing data from thousands of US Amazon users: The importance of transparency, money, and data use](https://mlsanity.com/paper/2404.13172/) (2024)

## Abstract (arXiv)

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.