June 26, 2024, 4:42 a.m. | Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, Tat-Seng Chua

cs.CL updates on arXiv.org arxiv.org

arXiv:2309.05519v3 Announce Type: replace-cross
Abstract: While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities. As we humans always perceive the world and communicate with people through various modalities, developing any-to-any MM-LLMs capable of accepting and delivering content in any modality becomes essential to human-level AI. To fill the gap, we present an end-to-end general-purpose any-to-any MM-LLM system, …

arxiv cs.ai cs.cl cs.lg gpt llm multimodal next replace type

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