AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
Authors: name: Jun Zhan, name: Junqi Dai, name: Jiasheng Ye, name: Yunhua Zhou, name: Dong Zhang, name: Zhigeng Liu, name: Xin Zhang, name: Ruibin Yuan, name: Ge Zhang, name: Linyang Li, name: Hang Yan, name: Jie Fu, name: Tao Gui, name: Tianxiang Sun, name: Yugang Jiang, name: Xipeng Qiu

We introduce AnyGPT, an any-to-any multimodal language model that utilizes
discrete representations for the unified processing of various modalities,
including speech, text, images, and music. AnyGPT can be trained stably without
any alterations to the current large language model (LLM) architecture or
training paradigms. Instead, it relies exclusively on data-level preprocessing,
facilitating the seamless integration of new modalities into LLMs, akin to the
incorporation of new languages. We build a multimodal text-centric dataset for
multimodal alignment pre-training. Utilizing generative models, we synthesize
the first large-scale any-to-any multimodal instruction dataset. It consists of
108k samples of multi-turn conversations that intricately interweave various
modalities, thus equipping the model to handle arbitrary combinations of
multimodal inputs and outputs. Experimental results demonstrate that AnyGPT is
capable of facilitating any-to-any multimodal conversation while achieving
performance comparable to specialized models across all modalities, proving
that discrete representations can effectively and conveniently unify multiple
modalities within a language model. Demos are shown in
https://junzhan2000.github.io/AnyGPT.github.io/