In this article, the authors demonstrate how to effectively use a frozen language model’s capabilities for multi-modal (picture and text) input and output.
They train the language model to learn a new [RET] token that stands in for an image for image-text retrieval.
It has a new multimodal conversation and reasoning skills in addition to the original text only LLM’s ability to create text.
They also highlight the capabilities of pretrained text-only LLMs on visually based tasks.
The authors present a proof-of-principality test using a PLM with a fully automatic image recognition and declarative speech recognition system.
Their goal is to systematically explore the different kinds of information processing seen in this paper.
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