Poster

Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text

Wanrong Zhu · Jack Hessel · Anas Awadalla · Samir Yitzhak Gadre · Jesse Dodge · Alex Fang · Youngjae Yu · Ludwig Schmidt · William Yang Wang · Yejin Choi

Great Hall & Hall B1+B2 (level 1) #310
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Tue 12 Dec 8:45 a.m. PST — 10:45 a.m. PST

Abstract:

In-context vision and language models like Flamingo support arbitrarily interleaved sequences of images and text as input.This format not only enables few-shot learning via interleaving independent supervised (image, text) examples, but also, more complex prompts involving interaction between images, e.g., ``What do image A and image B have in common?''To support this interface, pretraining occurs over web corpora that similarly contain interleaved images+text.To date, however, large-scale data of this form have not been publicly available.We release Multimodal C4, an augmentation of the popular text-only C4 corpus with images interleaved.We use a linear assignment algorithm to place images into longer bodies of text using CLIP features, a process that we show outperforms alternatives.Multimodal C4 spans everyday topics like cooking, travel, technology, etc. A manual inspection of a random sample of documents shows that a vast majority (88\%) of images are topically relevant, and that linear assignment frequently selects individual sentences specifically well-aligned with each image (80\%). After filtering NSFW images, ads, etc., the resulting corpus consists of 101.2M documents with 571M images interleaved in 43B English tokens.

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