Grounding Language Models to Images for Multimodal Inputs and Outputs

• URL: http://arxiv.org/abs/2301.13823v4
• Date: Tue, 13 Jun 2023 21:54:58 GMT
• Title: Grounding Language Models to Images for Multimodal Inputs and Outputs
• Authors: Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried
• Abstract summary: We propose an efficient method to ground pretrained text-only language models to the visual domain. We process arbitrarily interleaved image-and-text data, and generate text interleaved with retrieved images.
• Score: 89.30027812161686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an efficient method to ground pretrained text-only language models to the visual domain, enabling them to process arbitrarily interleaved image-and-text data, and generate text interleaved with retrieved images. Our method leverages the abilities of language models learnt from large scale text-only pretraining, such as in-context learning and free-form text generation. We keep the language model frozen, and finetune input and output linear layers to enable cross-modality interactions. This allows our model to process arbitrarily interleaved image-and-text inputs, and generate free-form text interleaved with retrieved images. We achieve strong zero-shot performance on grounded tasks such as contextual image retrieval and multimodal dialogue, and showcase compelling interactive abilities. Our approach works with any off-the-shelf language model and paves the way towards an effective, general solution for leveraging pretrained language models in visually grounded settings.

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