Recent multimodal models such as DALL-E and CM3 have achieved remarkable progress in text-to-image and image-to-text generation. However, these models store all learned knowledge (e.g., the appearance of the Eiffel Tower) in the model parameters, requiring increasingly larger models and training data to capture more knowledge. To integrate knowledge in a more scalable and modular way, we propose a retrieval-augmented multimodal model, which enables a base multimodal model (generator) to refer to relevant text and images fetched by a retriever from external memory (e.g., documents on the web). Specifically, for the retriever, we use a pretrained CLIP, and for the generator, we train a CM3 Transformer on the LAION dataset. Our resulting model, named Retrieval-Augmented CM3 (RA-CM3), is the first multimodal model that can retrieve and generate both text and images. We show that RA-CM3 significantly outperforms baseline multimodal models such as DALL-E and CM3 on both image and caption generation tasks (12 FID and 17 CIDEr improvements on MS-COCO), while requiring much less compute for training (<30% of DALL-E). Moreover, we show that RA-CM3 exhibits novel capabilities, such as faithful image generation and multimodal in-context learning (e.g., image generation from demonstrations).
@article{arxiv.2211.12561,
title = {Retrieval-Augmented Multimodal Language Modeling},
author = {Michihiro Yasunaga and Armen Aghajanyan and Weijia Shi and Rich James and Jure Leskovec and Percy Liang and Mike Lewis and Luke Zettlemoyer and Wen-tau Yih},
journal= {arXiv preprint arXiv:2211.12561},
year = {2023}
}
Comments
Published at ICML 2023. Blog post available at https://cs.stanford.edu/~myasu/blog/racm3/