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We propose an efficient method to ground pretrained text-only language modelsto the visual domain, enabling them to process arbitrarily interleavedimage-and-text data, and generate text interleaved with retrieved images. Ourmethod leverages the abilities of language models learnt from large scaletext-only pretraining, such as in-context learning and free-form textgeneration. We keep the language model frozen, and finetune input and outputlinear layers to enable cross-modality interactions. This allows our model toprocess arbitrarily interleaved image-and-text inputs, and generate free-formtext interleaved with retrieved images. We achieve strong zero-shot performanceon grounded tasks such as contextual image retrieval and multimodal dialogue,and showcase compelling interactive abilities. Our approach works with anyoff-the-shelf language model and paves the way towards an effective, generalsolution for leveraging pretrained language models in visually groundedsettings.

Grounding Language Models to Images for Multimodal Inputs and Outputs