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

arXiv: https://arxiv.org/abs/2406.13923 · HTML: https://mlsanity.com/paper/2406.13923/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 18 of 18)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://mlsanity.com/paper/2102.08981/) (2021)
- [Compressive Transformers for Long-Range Sequence Modelling](https://mlsanity.com/paper/1911.05507/) (2019)
- [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://mlsanity.com/paper/2306.16527/) (2023)
- [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023)
- [Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text](https://mlsanity.com/paper/2304.06939/) (2023)
- [KOSMOS-2.5: A Multimodal Literate Model](https://mlsanity.com/paper/2309.11419/) (2023)
- [E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning](https://mlsanity.com/paper/2106.01804/) (2021)
- [Masked Vision and Language Modeling for Multi-modal Representation Learning](https://mlsanity.com/paper/2208.02131/) (2022)
- [CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark](https://mlsanity.com/paper/2401.11944/) (2024)
- [InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining](https://mlsanity.com/paper/2003.13198/) (2020)
- [MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model](https://mlsanity.com/paper/2210.05335/) (2022)

## Built on by (most cited first, 7 of 7)

- [Baichuan-Omni-1.5 Technical Report](https://mlsanity.com/paper/2501.15368/) (2025)
- [Mavors: Multi-granularity Video Representation for Multimodal Large Language Model](https://mlsanity.com/paper/2504.10068/) (2025)
- [PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning](https://mlsanity.com/paper/2506.14907/) (2025)
- [2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining](https://mlsanity.com/paper/2501.00958/) (2025)
- [AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language Model](https://mlsanity.com/paper/2510.11496/) (2025)
- [SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature](https://mlsanity.com/paper/2601.10108/) (2026)
- [MM-OptBench: A Solver-Grounded Benchmark for Multimodal Optimization Modeling](https://mlsanity.com/paper/2605.12154/) (2026)

## Abstract (arXiv)

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. To address these issues, we introduce PIN (Paired and INterleaved multimodal documents), a novel data format designed to foster a deeper integration of visual and textual knowledge. The PIN format uniquely combines semantically rich Markdown files, which preserve fine-grained textual structures, with holistic overall images that capture the complete document layout. Following this format, we construct and release two large-scale, open-source datasets: PIN-200M (~200 million documents) and PIN-14M (~14 million), compiled from diverse web and scientific sources in both English and Chinese. To maximize usability, we provide detailed statistical analyses and equip the datasets with quality signals, enabling researchers to easily filter and select data for specific tasks. Our work provides the community with a versatile data format and substantial resources, offering a foundation for new research in pre-training strategies and the development of more powerful knowledge-intensive LMMs.