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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.07526 |
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| _version_ | 1866929341375447040 |
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| author | Chen, Qi Geng, Xiubo Rosset, Corby Buractaon, Carolyn Lu, Jingwen Shen, Tao Zhou, Kun Xiong, Chenyan Gong, Yeyun Bennett, Paul Craswell, Nick Xie, Xing Yang, Fan Tower, Bryan Rao, Nikhil Dong, Anlei Jiang, Wenqi Liu, Zheng Li, Mingqin Liu, Chuanjie Li, Zengzhong Majumder, Rangan Neville, Jennifer Oakley, Andy Risvik, Knut Magne Simhadri, Harsha Vardhan Varma, Manik Wang, Yujing Yang, Linjun Yang, Mao Zhang, Ce |
| author_facet | Chen, Qi Geng, Xiubo Rosset, Corby Buractaon, Carolyn Lu, Jingwen Shen, Tao Zhou, Kun Xiong, Chenyan Gong, Yeyun Bennett, Paul Craswell, Nick Xie, Xing Yang, Fan Tower, Bryan Rao, Nikhil Dong, Anlei Jiang, Wenqi Liu, Zheng Li, Mingqin Liu, Chuanjie Li, Zengzhong Majumder, Rangan Neville, Jennifer Oakley, Andy Risvik, Knut Magne Simhadri, Harsha Vardhan Varma, Manik Wang, Yujing Yang, Linjun Yang, Mao Zhang, Ce |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07526 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels Chen, Qi Geng, Xiubo Rosset, Corby Buractaon, Carolyn Lu, Jingwen Shen, Tao Zhou, Kun Xiong, Chenyan Gong, Yeyun Bennett, Paul Craswell, Nick Xie, Xing Yang, Fan Tower, Bryan Rao, Nikhil Dong, Anlei Jiang, Wenqi Liu, Zheng Li, Mingqin Liu, Chuanjie Li, Zengzhong Majumder, Rangan Neville, Jennifer Oakley, Andy Risvik, Knut Magne Simhadri, Harsha Vardhan Varma, Manik Wang, Yujing Yang, Linjun Yang, Mao Zhang, Ce Information Retrieval 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. |
| title | MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2405.07526 |