LLaVA represents a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding, achieving impressive chat capabilities mimicking spirits of the multimodal GPT-4 and setting a new state-of-the-art accuracy on Science QA.
Abstract
Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks in the language domain, but the idea is less explored in the multimodal field.
- Multimodal Instruct Data.
We present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data.
- LLaVA Model.
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.
- Performance.
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%.
- Open-source.
We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.
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