An Integrated Data Processing Framework for Pretraining Foundation Models
This work addresses a domain-specific problem for researchers and practitioners in AI/ML by providing a unified tool to streamline data preparation for foundation models, though it is incremental as it builds on existing data processing concepts.
The paper tackles the repetitive and cumbersome process of manually curating and cleansing datasets for pretraining foundation models by proposing an integrated data processing framework, demonstrating its effectiveness through automated evaluation with ChatGPT and pretraining GPT-2 to improve data quality.
The ability of the foundation models heavily relies on large-scale, diverse, and high-quality pretraining data. In order to improve data quality, researchers and practitioners often have to manually curate datasets from difference sources and develop dedicated data cleansing pipeline for each data repository. Lacking a unified data processing framework, this process is repetitive and cumbersome. To mitigate this issue, we propose a data processing framework that integrates a Processing Module which consists of a series of operators at different granularity levels, and an Analyzing Module which supports probing and evaluation of the refined data. The proposed framework is easy to use and highly flexible. In this demo paper, we first introduce how to use this framework with some example use cases and then demonstrate its effectiveness in improving the data quality with an automated evaluation with ChatGPT and an end-to-end evaluation in pretraining the GPT-2 model. The code and demonstration videos are accessible on GitHub.