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MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels (2405.07526v1)

Published 13 May 2024 in cs.IR

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 LLMs. 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.

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Exploring MS MARCO Web Search: A Comprehensive Dataset for Web-Scale Information Retrieval

Introduction to MS MARCO Web Search Dataset

In the pursuit of refining search technologies and LLMs, datasets play a crucial role. Among the newer contributions to this field is the MS MARCO Web Search dataset. This dataset champions the cause of large-scale, information-rich data collection with millions of real-world user interactions in the form of clicked query-document pairs, originating from real search logs. It aims to not just enhance the models but also to provide a robust ground for new research directions in AI and search technology.

The Significance of Real Clicked Query-Data Pairs

The unique selling point of the MS MARCO Web Search dataset is its incorporation of real clicked query-data pairs. These are not just theoretical constructs but are derived from actual user interactions, which adds a layer of practicality and realism to the dataset. Here’s a breakdown of why this is crucial:

  • Real-World Application: Models trained on this dataset can better predict or understand real-user queries due to their training on real-world data.
  • Diversity of Data: It includes a variety of languages and query types, which enriches the model's ability to handle diverse inputs.
  • Volume and Veracity: With millions of data points, the dataset provides a broad foundation for testing and enhancing information retrieval systems.

Challenges Addressed by the Dataset

MS MARCO Web Search doesn't just supply data; it brings forward challenges inherent in modern web-scale retrieval systems:

  1. Handling Scale: The dataset’s vast size poses a challenge in processing and utilizing the information effectively within reasonable computational limits.
  2. Quality of Data: Ensuring that the high volume of data maintains a high quality and relevance requires careful curation and perhaps sophisticated filtering mechanisms.
  3. Diversity in Queries: Given the multilingual nature and varied informational needs reflected in the queries, models need to evolve to handle such diversity efficiently.

Future Implications for AI and Search Technologies

The introduction of a dataset like MS MARCO Web Search paves the way for numerous future research opportunities and practical applications:

  • Enhancement of Search Engines: By training on a dataset close to the operational data of search engines, improvements in accuracy, and user satisfaction can be achieved.
  • Development of Robust LLMs: LLMs can be better equipped to handle misinformation and the dynamic nature of languages and user interactions.
  • Cross-Discipline Innovations: The dataset could lead to interesting crossover innovations involving machine learning, linguistics, and information science.

Predictions and Speculations

With its comprehensive coverage and real-world data grounding, the MS MARCO Web Search dataset is likely to be a catalyst in AI and search technology advancements. We might see:

  • Improved Query Handling: More nuanced understanding and responses to user queries, especially in multilingual contexts.
  • Adaptive Learning Models: Models that adjust to new information and user behavior patterns more dynamically.
  • Ethical AI Development: Enhanced capabilities to handle data privacy and ethical considerations due to the realistic dataset base.

In conclusion, the MS MARCO Web Search dataset is not merely a larger pile of data. It is a thoughtfully curated resource aimed at confronting the present challenges and anticipating future needs in web-scale data handling and retrieval. This dataset is not just a tool for improvement but a potential harbinger of the next generation of search technologies and AI models.

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Authors (31)
  1. Qi Chen (194 papers)
  2. Xiubo Geng (36 papers)
  3. Corby Rosset (21 papers)
  4. Carolyn Buractaon (2 papers)
  5. Jingwen Lu (5 papers)
  6. Tao Shen (87 papers)
  7. Kun Zhou (217 papers)
  8. Chenyan Xiong (95 papers)
  9. Yeyun Gong (78 papers)
  10. Paul Bennett (17 papers)
  11. Nick Craswell (51 papers)
  12. Xing Xie (220 papers)
  13. Fan Yang (878 papers)
  14. Bryan Tower (3 papers)
  15. Nikhil Rao (34 papers)
  16. Anlei Dong (6 papers)
  17. Wenqi Jiang (15 papers)
  18. Zheng Liu (312 papers)
  19. Mingqin Li (4 papers)
  20. Chuanjie Liu (6 papers)
Citations (1)
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