Visual Instruction Tuning

TL;DR

This paper introduces LLaVA, a multimodal model trained on GPT-4 generated visual instruction data, achieving high performance in multimodal understanding and visual question answering tasks.

Contribution

It is the first to use language-only GPT-4 to generate multimodal instruction data for training a large vision-language model, LLaVA.

Findings

LLaVA exhibits impressive multimodal chat abilities.

Achieves 85.1% score on synthetic multimodal instruction dataset.

Sets a new state-of-the-art accuracy of 92.53% on Science QA.

Abstract

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.