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Summary of Textsquare: Scaling Up Text-centric Visual Instruction Tuning, by Jingqun Tang et al.


TextSquare: Scaling up Text-Centric Visual Instruction Tuning

by Jingqun Tang, Chunhui Lin, Zhen Zhao, Shu Wei, Binghong Wu, Qi Liu, Hao Feng, Yang Li, Siqi Wang, Lei Liao, Wei Shi, Yuliang Liu, Hao Liu, Yuan Xie, Xiang Bai, Can Huang

First submitted to arxiv on: 19 Apr 2024

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: Machine Learning (cs.LG)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
A novel approach for generating a massive, high-quality instruction-tuning dataset, called Square-10M, is introduced. This dataset is created using closed-source Multimodal Large Language Models (MLLMs) and consists of four steps: Self-Questioning, Answering, Reasoning, and Evaluation. The TextSquare model, trained on this dataset, surpasses open-source previous state-of-the-art Text-centric MLLMs and sets a new standard in text-centric benchmarks, including OCRBench (62.2%). Additionally, the study demonstrates the critical role of VQA reasoning data in offering comprehensive contextual insights for specific questions, improving accuracy and mitigating hallucinations.
Low GrooveSquid.com (original content) Low Difficulty Summary
A new way to make computer models better at understanding text is developed. This method creates a huge dataset with lots of examples to help train the model. The trained model, called TextSquare, does much better than other models on tests that ask it to answer questions based on text. It also gets rid of mistakes by providing more context and helping the model make sense of the text.

Keywords

» Artificial intelligence  » Instruction tuning