ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst

• URL: http://arxiv.org/abs/2305.16103v1
• Date: Thu, 25 May 2023 14:34:08 GMT
• Title: ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst
• Authors: Zijia Zhao, Longteng Guo, Tongtian Yue, Sihan Chen, Shuai Shao, Xinxin Zhu, Zehuan Yuan, Jing Liu
• Abstract summary: ChatBridge is a novel multimodal language model that leverages the expressive capabilities of language to bridge the gap between various modalities. All codes, data, and models of ChatBridge will be open-sourced.
• Score: 24.517389691825667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building general-purpose models that can perceive diverse real-world modalities and solve various tasks is an appealing target in artificial intelligence. In this paper, we present ChatBridge, a novel multimodal language model that leverages the expressive capabilities of language as the catalyst to bridge the gap between various modalities. We show that only language-paired two-modality data is sufficient to connect all modalities. ChatBridge leverages recent large language models (LLM) and extends their zero-shot capabilities to incorporate diverse multimodal inputs. ChatBridge undergoes a two-stage training. The first stage aligns each modality with language, which brings emergent multimodal correlation and collaboration abilities. The second stage instruction-finetunes ChatBridge to align it with user intent with our newly proposed multimodal instruction tuning dataset, named MULTIS, which covers a wide range of 16 multimodal tasks of text, image, video, and audio modalities. We show strong quantitative and qualitative results on zero-shot multimodal tasks covering text, image, video, and audio modalities. All codes, data, and models of ChatBridge will be open-sourced.

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