MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Abstract
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.
Keywords
Cite
@article{arxiv.2405.07526,
title = {MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels},
author = {Qi Chen and Xiubo Geng and Corby Rosset and Carolyn Buractaon and Jingwen Lu and Tao Shen and Kun Zhou and Chenyan Xiong and Yeyun Gong and Paul Bennett and Nick Craswell and Xing Xie and Fan Yang and Bryan Tower and Nikhil Rao and Anlei Dong and Wenqi Jiang and Zheng Liu and Mingqin Li and Chuanjie Liu and Zengzhong Li and Rangan Majumder and Jennifer Neville and Andy Oakley and Knut Magne Risvik and Harsha Vardhan Simhadri and Manik Varma and Yujing Wang and Linjun Yang and Mao Yang and Ce Zhang},
journal= {arXiv preprint arXiv:2405.07526},
year = {2024}
}
Comments
10 pages, 6 figures, for associated dataset, see http://github.com/microsoft/MS-MARCO-Web-Search