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

• URL: http://arxiv.org/abs/2308.10755v3
• Date: Fri, 15 Sep 2023 09:52:14 GMT
• Title: WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models
• Authors: Conghui He, Zhenjiang Jin, Chao Xu, Jiantao Qiu, Bin Wang, Wei Li, Hang Yan, Jiaqi Wang, Dahua Lin
• Abstract summary: "Wan Juan" is a large-scale multimodal dataset composed of both Chinese and English data, collected from a wide range of web sources. 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.
• Score: 69.96148259273065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models [146.18107944503436]
  Molmo is a new family of VLMs that are state-of-the-art in their class of openness. Our key innovation is a novel, highly detailed image caption dataset collected entirely from human annotators. We will be releasing all of our model weights, captioning and fine-tuning data, and source code in the near future.
  arXiv  Detail & Related papers  (2024-09-25T17:59:51Z)
• CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare [12.218718086529462]
  This study focuses on the Comprehensive Medical Benchmark in Chinese (CMB) We successfully trained a smaller base model to achieve scores comparable to larger models. By integrating a wide range of instructional content, our approach addresses potential issues such as data quality inconsistencies.
  arXiv  Detail & Related papers  (2024-07-29T05:00:48Z)
• Semantic-Aware Representation of Multi-Modal Data for Data Ingress: A Literature Review [1.8590097948961688]
  Generative AI such as Large Language Models (LLMs) sees broad adoption to process multi-modal data such as text, images, audio, and video. Managing this data efficiently has become a significant practical challenge in the industry-double as much data is not double as good. This study focuses on the different semantic-aware techniques to extract embeddings from mono-modal, multi-modal, and cross-modal data.
  arXiv  Detail & Related papers  (2024-07-17T09:49:11Z)
• X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment [4.571088742209442]
  We create a 91K English-Korean-Chinese multilingual, multimodal training dataset. We develop a bilingual multimodal model that exhibits excellent performance in both Korean and English.
  arXiv  Detail & Related papers  (2024-03-18T01:14:47Z)
• EXMODD: An EXplanatory Multimodal Open-Domain Dialogue dataset [20.445453185198186]
  We propose a Multimodal Data Construction Framework (MDCF) to alleviate the significant human and resource expenditure in data collection. MDCF automatically provides explanation for a given image and its corresponding dialogue, which can provide a certain degree of interpretability. Experiments indicate a positive correlation between the model's ability to generate accurate understandings and high-quality responses.
  arXiv  Detail & Related papers  (2023-10-17T03:28:29Z)
• Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants [65.47222691674074]
  Muffin framework employs pre-trained vision-language models to act as providers of visual signals. UniMM-Chat dataset explores the complementarities of datasets to generate 1.1M high-quality and diverse multimodal instructions.
  arXiv  Detail & Related papers  (2023-10-01T12:35:18Z)
• Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages [76.35234803589412]
  MPM is an effective training paradigm for training large multimodal models in non-English languages. We build large multimodal models VisCPM in image-to-text and text-to-image generation, which achieve state-of-the-art (open-source) performance in Chinese.
  arXiv  Detail & Related papers  (2023-08-23T09:55:41Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• Enhancing Chat Language Models by Scaling High-quality Instructional Conversations [91.98516412612739]
  We first provide a systematically designed, diverse, informative, large-scale dataset of instructional conversations, UltraChat. Our objective is to capture the breadth of interactions that a human might have with an AI assistant. We fine-tune a LLaMA model to create a powerful conversational model, UltraLLaMA.
  arXiv  Detail & Related papers  (2023-05-23T16:49:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.