  Text-centric visual question answering (VQA) has made great strides with the
development of Multimodal Large Language Models (MLLMs), yet open-source models
still fall short of leading models like GPT4V and Gemini, partly due to a lack
of extensive, high-quality instruction tuning data. To this end, we introduce a
new approach for creating a massive, high-quality instruction-tuning dataset,
Square-10M, which is generated using closed-source MLLMs. The data construction
process, termed Square, consists of four steps: Self-Questioning, Answering,
Reasoning, and Evaluation. Our experiments with Square-10M led to three key
findings: 1) Our model, TextSquare, considerably surpasses open-source previous
state-of-the-art Text-centric MLLMs and sets a new standard on OCRBench(62.2%).
It even outperforms top-tier models like GPT4V and Gemini in 6 of 10
text-centric benchmarks. 2) Additionally, we demonstrate the critical role of
VQA reasoning data in offering comprehensive contextual insights for specific
questions. This not only improves accuracy but also significantly mitigates
hallucinations. Specifically, TextSquare scores an average of 75.1% across four
general VQA and hallucination evaluation datasets, outperforming previous
state-of-the-art models. 3) Notably, the phenomenon observed in scaling
text-centric VQA datasets reveals a vivid pattern: the exponential increase of
instruction tuning data volume is directly proportional to the improvement in
model performance, thereby validating the necessity of the dataset scale and
the high quality of Square-10M.
