Instruction tuning large language models (LLMs)  using machine-generated instruction-following data has been shown to improve zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field.
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:LargeLanguageandVisionAssistant, an end-to-end trained large multimodal model that connects a vision encoder and an LLM for general-purpose visual and language understanding.
To facilitate future research on visual instruction following, we construct two evaluation benchmarks with diverse and challenging application-oriented tasks.
Our experiments show that LLaVA demonstrates impressive multimodal 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 publicly available.