LLaVA: Large Language and Vision Assistant

Visual Instruction Tuning

NeurIPS 2023 (Oral)
University of Wisconsin-Madison Microsoft Research Columbia University
*Equal Contribution

πŸ”₯[NEW!] LLaVA-1.5 achieves SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods that use billion-scale data.

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.


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.

  1. Multimodal Instruct Data. We present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data.
  2. 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.
  3. 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%.
  4. Open-source. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.

Multimodal Instrucion-Following Data

Based on the COCO dataset, we interact with language-only GPT-4, and collect 158K unique language-image instruction-following samples in total, including 58K in conversations, 23K in detailed description, and 77k in complex reasoning, respectively. Please check out ``LLaVA-Instruct-150K''' on [HuggingFace Dataset].

Data file name File Size Sample Size
conversation_58k.json 126 MB 58K
detail_23k.json 20.5 MB 23K
complex_reasoning_77k.json 79.6 MB 77K

For each subset, we visualize the root noun-verb pairs for the instruction and response. For each chart, please click the link for the interactive page to check out the noun-verb pairs whose frequency is higher the given number.

Instruction: Conversation [0, 20, 50]
Instruction: Detailed Description [0]
Instruction: Complex Reasoning [0, 20, 50]
Response: Conversation [0, 20, 50]
Response: Detailed Description [0, 20, 50]
Response: Complex Reasoning [0, 20, 50]

LLaVA: Large Language-and-Vision Assistant

LLaVa connects pre-trained CLIP ViT-L/14 visual encoder and large language model Vicuna, using a simple projection matrix. We consider a two-stage instruction-tuning procedure:

  • Stage 1: Pre-training for Feature Alignment. Only the projection matrix is updated, based on a subset of CC3M.
  • Stage 2: Fine-tuning End-to-End. Both the projection matrix and LLM are updated for two different use senarios:
    • Visual Chat: LLaVA is fine-tuned on our generated multimodal instruction-following data for daily user-oriented applications.
    • Science QA: LLaVA is fine-tuned on this multimodal reasonsing dataset for the science domain.
Please check out our [Model Zoo].


Visual Chat: Towards building multimodal GPT-4 level chatbot

An evaluation dataset with 30 unseen images is constructed: each image is assocaited with three types of instructions: conversation, detailed description and complex reasoning. This leads to 90 new language-image instructions, on which we test LLaVA and GPT-4, and use GPT-4 to rate their responses from score 1 to 10. The summed score and relative score per type is reported. Overall, LLaVA achieves 85.1% relative score compared with GPT-4, indicating the effectinvess of the proposed self-instruct method in multimodal settings

Science QA: New SoTA with the synergy of LLaVA with GPT-4

LLaVA alones achieve 90.92%. We use the text-only GPT-4 as the judge, to predict the final answer based on its own previous answers and the LLaVA answers. This "GPT-4 as judge" scheme yields a new SOTA 92.53%.

Examples on Visual Instruction Following

Visual Reasoning on two examples from OpenAI GPT-4 Technical Report

Optical character recognition (OCR)


          author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
          title={Improved Baselines with Visual Instruction Tuning}, 

    author      = {Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
    title       = {Visual Instruction Tuning},
    booktitle   = {NeurIPS},
    year        = {2023}


This website is adapted from Nerfies, licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. We thank the LLaMA team for giving us access to their models, and open-source projects, including Alpaca and Vicuna.

Usage and License Notices: The data, code and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of CLIP, LLaMA, Vicuna and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.

Related Links: [REACT] [GLIGEN] [Computer Vision in the Wild (CVinW)] [Insutrction Tuning with GPT-4]