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- W4386907923 abstract "Large language models with instruction-following abilities have revolutionized the field of artificial intelligence. These models show exceptional generalizability to tackle various real-world tasks through their natural language interfaces. However, their performance heavily relies on high-quality exemplar data, which is often difficult to obtain. This challenge is further exacerbated when it comes to multimodal instruction following. We introduce TextBind, an almost annotation-free framework for empowering larger language models with the multi-turn interleaved multimodal instruction-following capabilities. Our approach requires only image-caption pairs and generates multi-turn multimodal instruction-response conversations from a language model. To accommodate interleaved image-text inputs and outputs, we devise MIM, a language model-centric architecture that seamlessly integrates image encoder and decoder models. We release our dataset, model, and demo to foster future research in the area of multimodal instruction following." @default.
- W4386907923 created "2023-09-21" @default.
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- W4386907923 date "2023-09-14" @default.
- W4386907923 modified "2023-10-12" @default.
- W4386907923 title "TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild" @default.
- W4386907923 doi "https://doi.org/10.48550/arxiv.2309.08637" @default.
- W4386907923 hasPublicationYear "2023" @default.
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