OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
Harbin Institute of Technology · Nanjing University · Fudan University · Shanghai AI Laboratory · Research and Shanghai AI Laboratory · Shanghai Artificial Intelligence Laboratory · Shanghai AI Laboratory, Shanghai Jiaotong University · Shanghai Aritifcal Intelligence Laboratory · Shanghai Jiaotong University · Xi'an Jiaotong University · Sensetime Research Institute · Zhejiang University · Shanghai AI Lab · The Chinese University of Hong Kong, The Chinese University of Hong Kong · South China University of Technology, Tsinghua University · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Chinese Academy of Sciences · Toyota Technnological Institute · Tsinghua University, Tsinghua University
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摘要
Image-text interleaved data, consisting of multiple images and texts arranged in a natural document format, aligns with the presentation paradigm of internet data and closely resembles human reading habits. Recent studies have shown that such data aids multimodal in-context learning and maintains the capabilities of large language models during multimodal fine-tuning. However, the limited scale and diversity of current image-text interleaved data restrict the development of multimodal large language models. In this paper, we introduce OmniCorpus, a 10 billion-scale image-text interleaved dataset. Using an efficient data engine, we filter and extract large-scale high-quality documents, which contain 8.6 billion images and 1,696 billion text tokens. Compared to counterparts (e.g., MMC4, OBELICS), our dataset 1) has 15 times larger scales while maintaining good data quality; 2) features more diverse sources, including both English and non-English websites as well as video-centric websites; 3) is more flexible, easily degradable from an image-text interleaved format to pure text corpus and image-text pairs. Through comprehensive analysis and experiments, we validate the quality, usability, and effectiveness of the proposed dataset. We hope this could provide a solid data foundation for future multimodal model research.