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.

Related papers

• Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy [2.294223504228228]
  Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems. Inspired by the human ability to assimilate information through many senses, this method enables applications such as text-to-video conversion, visual question answering, and image captioning. Recent developments in datasets that support multimodal language models (MLLMs) are highlighted in this overview.
  arXiv  Detail & Related papers  (2024-12-23T18:15:19Z)
• Multi-modal Retrieval Augmented Multi-modal Generation: Datasets, Evaluation Metrics and Strong Baselines [64.61315565501681]
  Multi-modal Retrieval Augmented Multi-modal Generation (M$2$RAG) is a novel task that enables foundation models to process multi-modal web content. Despite its potential impact, M$2$RAG remains understudied, lacking comprehensive analysis and high-quality data resources.
  arXiv  Detail & Related papers  (2024-11-25T13:20:19Z)
• MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct [148.39859547619156]
  We propose MMEvol, a novel multimodal instruction data evolution framework. MMEvol iteratively improves data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution. Our approach reaches state-of-the-art (SOTA) performance in nine tasks using significantly less data compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-09T17:44:00Z)
• HEMM: Holistic Evaluation of Multimodal Foundation Models [91.60364024897653]
  Multimodal foundation models can holistically process text alongside images, video, audio, and other sensory modalities. It is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains.
  arXiv  Detail & Related papers  (2024-07-03T18:00:48Z)
• From Efficient Multimodal Models to World Models: A Survey [28.780451336834876]
  Multimodal Large Models (MLMs) are becoming a significant research focus combining powerful language models with multimodal learning. This review explores the latest developments and challenges in large instructions, emphasizing their potential in achieving artificial general intelligence.
  arXiv  Detail & Related papers  (2024-06-27T15:36:43Z)
• Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning [51.80447197290866]
  Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning. Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies. We introduce a novel framework with Mixture of Modality Knowledge experts (MoMoK) to learn adaptive multi-modal entity representations.
  arXiv  Detail & Related papers  (2024-05-27T06:36:17Z)
• Generative Multi-Modal Knowledge Retrieval with Large Language Models [75.70313858231833]
  We propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases. We demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
  arXiv  Detail & Related papers  (2024-01-16T08:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.