LLaVAR: Enhanced Visual Instruction Tuning

for Text-rich Image Understanding

Georgia Tech Adobe Research Stanford University

LLaVAR: The LLaVA model that can Read texts within images.


Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans. Furthermore, recent instruction-following datasets include images as visual inputs, collecting responses for image-based instructions. However, visual instruction-tuned models cannot comprehend textual details within images well. This work enhances the current visual instruction tuning pipeline with text-rich images (e.g., movie posters, book covers, etc.). Specifically, we first use publicly available OCR tools to collect results on 422K text-rich images from the LAION dataset. Moreover, we prompt text-only GPT-4 with recognized texts and image captions to generate 16K conversations, each containing question-answer pairs for text-rich images. By combining our collected data with previous multi-modal instruction-following data, our model, LLaVAR, substantially improves the LLaVA model's capability on text-based VQA datasets (up to 20% accuracy improvement) while achieving an accuracy of 91.42% on ScienceQA. The GPT-4-based instruction-following evaluation also demonstrates the improvement of our model on both natural images and text-rich images. Through qualitative analysis, LLaVAR shows promising interaction (e.g., reasoning, writing, and elaboration) skills with humans based on the latest real-world online content that combines text and images.

[UPDATE 08/01] Check out the ready-to-use model checkpoint and finetuning dataset from the community on Huggingface!

[UPDATE 07/21] Release the metadata of used LAION images.

[UPDATE 07/12] Release the OCR evaluation results/script on the MME benchmark. LLaVAR increases the OCR score of LLaVA from 50 to 80.

[UPDATE 07/05] Data available on Huggingface.

[UPDATE 07/05] Model checkpoint (delta) available on Huggingface.

[UPDATE 06/29] Initial Release of Data/Code/Model. Huggingface is on the way!

Enhanced Visual Instruction Data with Text-Rich Images

Based on the LAION dataset, we collect 422K pretraining data based on OCR results. For finetuning data, we collect 16K high-quality instruction-following data by interacting with langauge-only GPT-4. Note that we also release a larger and more diverse finetuning dataset below (20K), which contains the 16K we used for the paper. The instruction files below contain the original LLaVA instructions. You can directly use them after merging the images into your LLaVA image folders. If you want to use them independently, you can remove the items contained in the original chat.json and llava_instruct_150k.json from LLaVA.

Data file name File Size Sample Size
Pretraining images 12.5 GB 422K
Pretraining instructions 388.7 MB 595K + 422K
Finetuning images 574.8 MB 20K
Finetuning instructions - 16K 255.5 MB 158K + 16K
Finetuning instructions - 20K 259.4 MB 158K + 20K

Images are all text-rich images from LAION, where we apply different filtering rules for pretraining and finetuning.

Pretraining Images
Finetuning Images

We visualize the root verb-noun pairs and objects that are being asked in instructions generated by GPT-4.

Verb-Noun Pairs


We evaluate the performance of LLaVAR on four text-based VQA datasets: ST-VQA, OCR-VQA, TextVQA, and DocVQA. LLaVAR also achieves an accuracy of 91.42% on ScienceQA.

Evaluation Data

We collect 50 instruction-following data on 50 text-rich images from LAION. You can use it for GPT-4-based instruction-following evaluation.

More Examples

We show more instruction-following examples below:


  title={LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding}, 
  author={Yanzhe Zhang and Ruiyi Zhang and Jiuxiang Gu and Yufan Zhou and Nedim Lipka and Diyi Yang and Tong Sun},


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, Vicuna, and LLaVA.

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, GPT-4 and LLaVA. 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.