TextSquare: Scaling up Text-Centric Visual Instruction Tuning

arXiv:2404.12803v1 Announce Type: cross
Abstract: 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, …

abstract arxiv cs.cv cs.lg data development gemini gpt4v language language models large language large language models mllms multimodal open-source models quality question question answering scaling scaling up text type visual vqa