Meta tags:
description=In search of the next generation of multimodal datasets;
author=Sarah Pratt;

Headings (most frequently used words):

language, reasoning, datacomp, contrastive, image, pre, training, modeling, chain, of, thought, and, vision, models, clip, lm, vlms,

Text of the page (most frequently used words):
the (11), set (7), model (6), language (6), best (5), evaluate (4), training (4), testing (4), downstream (4), models (4), and (3), subset (3), from (3), large (3), pool (3), train (3), your (3), tasks (3), vision (3), data (3), reasoning (3), select (3), datacomp (3), multimodal (2), pairs (2), text (2), clip (2), image (2), filter, mix, vlms, generate, teach, base, these, them, domains, reason, question, answer, chain, thought, modeling, contrastive, pre, machine, learning, benchmark, where, are, fixed, challenge, find, possible, setting, learn, more, about, how, participate, welcome,

Text of the page (random words):
datacomp datacomp welcome to datacomp the machine learning benchmark where the models are fixed and the challenge is to find the best possible data select a setting to learn more about how to participate clip contrastive language image pre training select the best subset of image text pairs from a large pool to train a clip model evaluate your training set by testing the model on a set of downstream vision tasks lm language modeling select the best subset of text data from a large pool to train a language model evaluate your training set by testing the model on a set of downstream language tasks reasoning chain of thought and reasoning generate the best question answer pairs to teach base models to reason evaluate these models by testing them on a set of downstream reasoning domains vlms vision language models filter and mix the best subset of multimodal data from a large pool to train a vision language model evaluate your training set by testing the model on a set of downstream multimodal tasks