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arXiv · 2308.10755

WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models

Conghui He·Zhenjiang Jin·Chao Xu·Jiantao Qiu·Bin Wang·Wei Li·Hang Yan·Jiaqi Wang·Dahua Lin
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Abstract

The rise in popularity of ChatGPT and GPT-4 has significantly accelerated the development of large models, leading to the creation of numerous impressive large language models(LLMs) and multimodal large language models (MLLMs). These cutting-edge models owe their remarkable performance to high-quality data. However, the details of the training data used in leading paradigms are often kept confidential. This lack of transparency, coupled with the scarcity of open-source data, impedes further developments within the community. As a response, this paper presents "Wan Juan", a large-scale multimodal dataset composed of both Chinese and English data, collected from a wide range of web sources. The dataset incorporates text, image-text, and video modalities, with a total volume exceeding 2TB. It was utilized in the training of InternLM, a model that demonstrated significant advantages in multi-dimensional evaluations when compared to models of a similar scale. All data can be accessed at https://opendatalab.org.cn/WanJuan1.0.

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Conghui He, Zhenjiang Jin, Chao Xu, Jiantao Qiu, Bin Wang, Wei Li, Hang Yan, Jiaqi Wang, Dahua Lin. 2023-08-21. WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models. https://arxiv.org/abs/2308.10755

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