An Integrated Data Processing Framework for Pretraining Foundation Models

arXiv:2402.16358v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.cl cs.ir cs.lg data data cleansing data processing data quality data repository datasets difference diverse foundation framework pipeline pretraining processing quality researchers scale type