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.

翻译：近年来大模型的突破凸显了数据规模、标签和模态的关键重要性。本文介绍了MS MARCO Web搜索——首个大规模信息丰富的网络数据集，包含数百万个真实点击的查询-文档标签。该数据集紧密模拟真实网络文档与查询分布，为各类下游任务提供丰富信息，并推动通用端到端神经索引模型、通用嵌入模型以及基于大语言模型的下一代信息访问系统等领域的研究。MS MARCO Web搜索提供了一个包含三项网络检索挑战任务的评测基准，要求在机器学习和信息检索系统研究领域均实现创新。作为首个满足大规模、真实与丰富数据需求的数据集，MS MARCO Web搜索为人工智能与系统研究未来进展铺平了道路。MS MARCO Web搜索数据集地址：https://github.com/microsoft/MS-MARCO-Web-Search。