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

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arxiv 2308.10755 v3 pith:5PAWPQFC submitted 2023-08-21 cs.CL cs.CV

Conghui He , Zhenjiang Jin , Chao Xu , Jiantao Qiu , Bin Wang , Wei Li , Hang Yan , Jiaqi Wang
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Dahua Lin
This is my paper
classification cs.CLcs.CV
keywords modelsdatalargedatasetmultimodalchineseenglishlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. DataComp-VLM: Improved Open Datasets for Vision-Language Models

    cs.CV 2026-06 conditional novelty 8.0 of 10

    DataComp-VLM benchmark shows instruction-heavy data mixing outperforms filtering for VLM training, with DCVLM-Baseline achieving 63.6% on 33 tasks for 8B models (+5.4pp over FineVision).

  2. DataComp-VLM: Improved Open Datasets for Vision-Language Models

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    DataComp-VLM benchmark shows instruction-heavy data mixtures outperform caption-heavy ones for VLM training, with DCVLM-Baseline reaching 63.6% on 33 tasks using 200B tokens, +5.4pp over FineVision.

  3. Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

    cs.CV 2024-12 unverdicted novelty 6.0 of 10

    InternVL 2.5 is the first open-source MLLM to surpass 70% on the MMMU benchmark via model, data, and test-time scaling, with a 3.7-point gain from chain-of-thought reasoning.

  4. InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

    cs.CV 2023-07 unverdicted novelty 6.0 of 10

    InternVid supplies 7M videos and LLM captions to train ViCLIP, which reaches leading zero-shot action recognition and competitive retrieval performance.

  5. DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

    cs.CV 2024-12 accept novelty 5.0 of 10

    DeepSeek-VL2 is a series of MoE vision-language models using dynamic tiling and latent attention that reach competitive or state-of-the-art results on VQA, OCR, document understanding and grounding with 1.0B to 4.5B a...

  6. InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output

    cs.CV 2024-07 conditional novelty 5.0 of 10

    InternLM-XComposer-2.5 is a 7B vision-language model supporting up to 96K context that reaches GPT-4V-level performance on image, video, and multi-turn tasks and adds LoRA-driven text-image composition capabilities.

  7. InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model

    cs.CV 2024-01 unverdicted novelty 5.0 of 10

    InternLM-XComposer2 introduces Partial LoRA on InternLM2-7B to enable high-quality free-form text-image composition while matching or exceeding GPT-4V on select vision-language benchmarks.

  8. Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

    cs.CL 2026-06 unverdicted novelty 4.0 of 10

    Technical report announcing Ling-2.6 and Ring-2.6 models with hybrid linear attention, evolutionary CoT, and KPop RL for efficient agentic intelligence at scale.

  9. How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

    cs.CV 2024-04 unverdicted novelty 4.0 of 10

    InternVL 1.5 narrows the performance gap to proprietary multimodal models via a stronger transferable vision encoder, dynamic high-resolution tiling, and curated English-Chinese training data.

  10. InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition

    cs.CV 2023-09 conditional novelty 4.0 of 10

    InternLM-XComposer generates articles with seamlessly integrated images and achieves state-of-the-art results on vision-language benchmarks including MME, MMBench, and Seed-Bench.

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