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We introduce CM3, a family of causally masked generative models trained overa large corpus of structured multi-modal documents that can contain both textand image tokens. Our new causally masked approach generates tokens left toright while also masking out a small number of long token spans that aregenerated at the end of the string, instead of their original positions. Thecasual masking object provides a type of hybrid of the more common causal andmasked language models, by enabling full generative modeling while alsoproviding bidirectional context when generating the masked spans. We traincausally masked language-image models on large-scale web and Wikipediaarticles, where each document contains all of the text, hypertext markup,hyperlinks, and image tokens (from a VQVAE-GAN), provided in the order theyappear in the original HTML source (before masking). The resulting CM3 modelscan generate rich structured, multi-modal outputs while conditioning onarbitrary masked document contexts, and thereby implicitly learn a wide rangeof text, image, and cross modal tasks. They can be prompted to recover, in azero-shot fashion, the functionality of models such as DALL-E, GENRE, and HTLM.We set the new state-of-the-art in zero-shot summarization, entity linking, andentity disambiguation while maintaining competitive performance in thefine-tuning setting. We can generate images unconditionally, conditioned ontext (like DALL-E) and do captioning all in a zero-shot setting with a singlemodel.

CM3: A Causal Masked Multimodal Model of the Internet