arxiv preprint - TextSquare: Scaling up Text-Centric Visual Instruction Tuning

In this episode, we discuss 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. The paper describes advancements in text-centric visual question answering using a novel dataset called Square-10M, developed to improve Multimodal Large Language Models (MLLMs) through instruction tuning. The dataset, generated with closed-source MLLMs, employs a method named Square that covers Self-Questioning, Answering, Reasoning, and Evaluation for data construction. Experiments on the dataset indicated significant performance enhancements over existing models, highlighting the importance of the quantity of reasoning data in VQA for enhancing accuracy and reducing errors in model responses.