PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents

• URL: http://arxiv.org/abs/2406.13923v1
• Date: Thu, 20 Jun 2024 01:43:08 GMT
• Title: PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents
• Authors: Junjie Wang, Yin Zhang, Yatai Ji, Yuxiang Zhang, Chunyang Jiang, Yubo Wang, Kang Zhu, Zekun Wang, Tiezhen Wang, Wenhao Huang, Jie Fu, Bei Chen, Qunshu Lin, Minghao Liu, Ge Zhang, Wenhu Chen,
• Abstract summary: PIN format is built on three foundational principles: knowledge intensity, scalability, and support for diverse training modalities. We present PIN-14M, an open-source dataset comprising 14 million samples derived from a diverse range of Chinese and English sources.
• Score: 58.35492519636351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Large Multimodal Models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks. However, persistent challenges in perceptual and reasoning errors limit their efficacy, particularly in interpreting intricate visual data and deducing multimodal relationships. Addressing these issues, we introduce a novel dataset format, PIN (Paired and INterleaved multimodal documents), designed to significantly improve both the depth and breadth of multimodal training. The PIN format is built on three foundational principles: knowledge intensity, scalability, and support for diverse training modalities. This innovative format combines markdown files and comprehensive images to enrich training data with a dense knowledge structure and versatile training strategies. We present PIN-14M, an open-source dataset comprising 14 million samples derived from a diverse range of Chinese and English sources, tailored to include complex web and scientific content. This dataset is constructed meticulously to ensure data quality and ethical integrity, aiming to facilitate advanced training strategies and improve model robustness against common multimodal training pitfalls. Our initial results, forming the basis of this technical report, suggest significant potential for the PIN format in refining LMM performance, with plans for future expansions and detailed evaluations of its impact on model capabilities.

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