MS MARCO Web Search: A Large-scale Information-rich Web Dataset with Millions of Real Click Labels
- Qi Chen ,
- Xiubo Geng ,
- Corby Rosset ,
- Carolyn Buractaon ,
- Jingwen Lu ,
- Tao Shen ,
- Kun Zhou ,
- Chenyan Xiong ,
- Yeyun Gong ,
- Paul Bennett ,
- Nick Craswell ,
- Xing Xie ,
- Fan Yang ,
- Bryan Tower ,
- Zheng Liu ,
- Mingqin Li ,
- Chuanjie Liu ,
- Jason (Zengzhong) Li ,
- Rangan Majumder ,
- Jennifer Neville ,
- Harsha Simhadri ,
- Manik Varma ,
- Yujing Wang ,
- Linjun Yang ,
- Mao Yang ,
- Ce Zhang
Published by WWW '24: Companion Proceedings of the ACM on Web Conference 2024 | Organized by ACM
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked query-document labels. This dataset closely mimics real-world web document and query distribution, provides rich information for various kinds of downstream tasks and encourages research in various areas, such as generic end-to-end neural indexer models, generic embedding models, and next generation information access system with large language models. MS MARCO Web Search offers a retrieval benchmark with three web retrieval challenge tasks that demands innovations in both machine learning and information retrieval system research domains. As the first dataset that meets large, real and rich data requirements, MS MARCO Web Search paves the way for future advancements in AI and system research. MS MARCO Web Search dataset is available at: https://github.com/microsoft/MS-MARCO-Web-Search.
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