TextBind: Multi-turn Interleaved Multimodal Instruction-following
Authors: name: Huayang Li, name: Siheng Li, name: Deng Cai, name: Longyue Wang, name: Lemao Liu, name: Taro Watanabe, name: Yujiu Yang, name: Shuming Shi

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. We release our dataset, model, and demo to foster future
research in the area of multimodal instruction following.