  Instruction tuning large language models (LLMs) using machine-generated
instruction-following data has improved zero-shot capabilities on new tasks,
but the idea is less explored in the multimodal field. In this paper, we
present the first attempt to use language-only GPT-4 to generate multimodal
language-image instruction-following data. By instruction tuning on such
generated data, we introduce LLaVA: Large Language and Vision Assistant, an
end-to-end trained large multimodal model that connects a vision encoder and
LLM for general-purpose visual and language understanding.Our early experiments
show that LLaVA demonstrates impressive multimodel chat abilities, sometimes
exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and
yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal
instruction-following dataset. When fine-tuned on Science QA, the synergy of
LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make
GPT-4 generated visual instruction tuning data, our model and code base
publicly available.
