MS MARCO Web Search: a Large-scale Information-rich Web Dataset with
  Millions of Real Click Labels
Authors: name: Qi Chen, name: Xiubo Geng, name: Corby Rosset, name: Carolyn Buractaon, name: Jingwen Lu, name: Tao Shen, name: Kun Zhou, name: Chenyan Xiong, name: Yeyun Gong, name: Paul Bennett, name: Nick Craswell, name: Xing Xie, name: Fan Yang, name: Bryan Tower, name: Nikhil Rao, name: Anlei Dong, name: Wenqi Jiang, name: Zheng Liu, name: Mingqin Li, name: Chuanjie Liu, name: Zengzhong Li, name: Rangan Majumder, name: Jennifer Neville, name: Andy Oakley, name: Knut Magne Risvik, name: Harsha Vardhan Simhadri, name: Manik Varma, name: Yujing Wang, name: Linjun Yang, name: Mao Yang, name: Ce Zhang

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 demand 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.