5 papers
20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone
DatologyAI, :, Siddharth Joshi +32
Data curation has shifted the quality-compute frontier for language-model and contrastive image-text pretraining, but its role for vision-language models (VLMs) is far less establi…
Reusing Pre-Training Data at Test Time is a Compute Multiplier
Alex Fang, Thomas Voice, Ruoming Pang +2
Large language models learn from their vast pre-training corpora, gaining the ability to solve an ever increasing variety of tasks; yet although researchers work to improve these d…
Datasets, Documents, and Repetitions: The Practicalities of Unequal Data Quality
Alex Fang, Hadi Pouransari, Matt Jordan +4
Data filtering has become a powerful tool for improving model performance while reducing computational cost. However, as large language model compute budgets continue to grow, the…
Language Models Improve When Pretraining Data Matches Target Tasks
David Mizrahi, Anders Boesen Lindbo Larsen, Jesse Allardice +7
Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies…
DataComp-LM: In search of the next generation of training sets for language models
Jeffrey Li, Alex Fang, Georgios Smyrnis +56
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardize…