  Despite Multi-modal Large Language Models (MM-LLMs) have made exciting
strides recently, they are still struggling to efficiently model the
interactions among multi-modal inputs and the generation in non-textual
modalities. In this work, we propose TEAL (Tokenize and Embed ALl)}, an
approach to treat the input from any modality as a token sequence and learn a
joint embedding space for all modalities. Specifically, for the input from any
modality, TEAL first discretizes it into a token sequence with the
off-the-shelf tokenizer and embeds the token sequence into a joint embedding
space with a learnable embedding matrix. MM-LLMs just need to predict the
multi-modal tokens autoregressively as the textual LLMs do. Finally, the
corresponding de-tokenizer is applied to generate the output in each modality
based on the predicted token sequence. With the joint embedding space, TEAL
enables the frozen LLMs to perform both understanding and generation tasks
involving non-textual modalities, such as image and audio. Thus, the textual
LLM can just work as an interface and maintain its high performance in textual
understanding and generation. Experiments show that TEAL achieves substantial
improvements in multi-modal understanding, and implements a simple scheme for
multi-modal generations.
