Scaling Laws for Data Filtering -- Data Curation cannot be Compute Agnostic

arXiv:2404.07177v1 Announce Type: new
Abstract: Vision-language models (VLMs) are trained for thousands of GPU hours on carefully curated web datasets. In recent times, data curation has gained prominence with several works developing strategies to retain 'high-quality' subsets of 'raw' scraped data. For instance, the LAION public dataset retained only 10% of the total crawled data. However, these strategies are typically developed agnostic of the available compute for training. In this paper, we first demonstrate that making filtering decisions independent of …

arxiv compute cs.lg curation data data curation filtering laws scaling type