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.
@article{arxiv.2308.10755,
title = {WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models},
author = {Conghui He and Zhenjiang Jin and Chao Xu and Jiantao Qiu and Bin Wang and Wei Li and Hang Yan and Jiaqi Wang and Dahua Lin},
journal= {arXiv preprint arXiv:2308.10755},
year = {2023}
}