Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and Related work 2.1 Web Scale Information Retrieval 2.1 Web Scale Information Retrieval 2.2 Existing Datasets 2.2 Existing Datasets 3 MS Marco Web Search Dataset and 3.1 Document Preparation 3 MS Marco Web Search Dataset and 3.1 Document Preparation 3.2 Query Selection and Labeling 3.2 Query Selection and Labeling 3.3 Dataset Analysis 3.3 Dataset Analysis 3.4 New Challenges Raised by MS MARCO Web Search 3.4 New Challenges Raised by MS MARCO Web Search 4 Benchmark Results and 4.1 Environment Setup 4 Benchmark Results and 4.1 Environment Setup 4.2 Baseline Methods 4.2 Baseline Methods 4.3 Evaluation Metrics 4.3 Evaluation Metrics 4.4 Evaluation of Embedding Models and 4.5 Evaluation of ANN Algorithms 4.4 Evaluation of Embedding Models and 4.5 Evaluation of ANN Algorithms 4.6 Evaluation of End-to-end Performance 4.6 Evaluation of End-to-end Performance 5 Potential Biases and Limitations 5 Potential Biases and Limitations 6 Future Work and Conclusions, and References 6 Future Work and Conclusions, and References 6 FUTURE WORK AND CONCLUSIONS MS MARCO Web Search is the first web dataset that effectively meets the criteria of being large, real, and rich in terms of data quality. It is composed of large-scale web pages and query-document labels sourced from a commercial search engine, retaining rich information about the web pages that is widely employed in industry. The retrieval benchmark offered by MS MARCO Web Search comprises three challenging tasks that require innovation in both the areas of machine learning and information retrieval system research. We hope MS MARCO Web Search can serve as a benchmark for modern web-scale information retrieval, facilitating future research and innovation in diverse directions. REFERENCES [1] [n. d.]. Billion-scale ANNS Benchmarks. https://big-ann-benchmarks.com/. [2] [n. d.]. Common Crawl. [3] [n. d.]. Robust04. https://trec.nist.gov/data/robust/04.guidelines.html. [4] Martin Aumüller, Erik Bernhardsson, and Alexander Faithfull. 2017. ANNbenchmarks: A benchmarking tool for approximate nearest neighbor algorithms. In International conference on similarity search and applications. Springer, 34–49. [5] Artem Babenko and Victor Lempitsky. 2014. The inverted multi-index. IEEE transactions on pattern analysis and machine intelligence 37, 6 (2014), 1247–1260. [6] Artem Babenko and Victor Lempitsky. 2016. Efficient indexing of billion-scale datasets of deep descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2055–2063. [7] Dmitry Baranchuk, Artem Babenko, and Yury Malkov. 2018. Revisiting the inverted indices for billion-scale approximate nearest neighbors. In Proceedings of the European Conference on Computer Vision (ECCV). 202–216. [8] Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Scott Yih, Sebastian Riedel, and Fabio Petroni. 2022. Autoregressive search engines: Generating substrings as document identifiers. Advances in Neural Information Processing Systems 35 (2022), 31668–31683. [9] Jamie Callan. 2012. The lemur project and its clueweb12 dataset. In Invited talk at the SIGIR 2012 Workshop on Open-Source Information Retrieval. [10] Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024. Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprint arXiv:2402.03216 (2024). [11] Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, and Jingdong Wang. 2021. SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search. Advances in Neural Information Processing Systems 34 (2021), 5199–5212. [12] Charles Clarke, Nick Craswell, and Ian Soboroff. 2004. Overview of the TREC 2004 Terabyte Track. In TREC. [13] Charles LA Clarke, Nick Craswell, and Ian Soboroff. 2009. Overview of the TREC 2009 Web Track.. In Trec, Vol. 9. 20–29. [14] Nick Craswell, Daniel Campos, Bhaskar Mitra, Emine Yilmaz, and Bodo Billerbeck. 2020. ORCAS: 20 million clicked query-document pairs for analyzing search. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2983–2989. [15] Zhuyun Dai and Jamie Callan. 2019. Context-aware sentence/passage term importance estimation for first stage retrieval. arXiv preprint arXiv:1910.10687 (2019). [16] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). [17] Luyu Gao and Jamie Callan. 2022. Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2843– 2853. [18] Jiafeng Guo, Yinqiong Cai, Yixing Fan, Fei Sun, Ruqing Zhang, and Xueqi Cheng. 2022. Semantic models for the first-stage retrieval: A comprehensive review. ACM Transactions on Information Systems (TOIS) 40, 4 (2022), 1–42. [19] Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar. 2020. Accelerating Large-Scale Inference with Anisotropic Vector Quantization. In Proceedings of the 37th International Conference on Machine Learning (ICML). 3887–3896. [20] Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. Advances in neural information processing systems 27 (2014). [21] Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2333–2338. [22] Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnawamy, and Rohan Kadekodi. 2019. Diskann: Fast accurate billion-point nearest neighbor search on a single node. Advances in Neural Information Processing Systems 32 (2019). [23] Herve Jegou, Matthijs Douze, and Cordelia Schmid. 2010. Product quantization for nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence 33, 1 (2010), 117–128. [24] Hervé Jégou, Romain Tavenard, Matthijs Douze, and Laurent Amsaleg. 2011. Searching in one billion vectors: re-rank with source coding. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 861–864. [25] Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data (2019). [26] Yannis Kalantidis and Yannis Avrithis. 2014. Locally optimized product quantization for approximate nearest neighbor search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2321–2328. [27] Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for opendomain question answering. arXiv preprint arXiv:2004.04906 (2020). [28] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics 7 (2019), 453–466. [29] Carlos Lassance and Stéphane Clinchant. 2023. Naver Labs Europe (SPLADE)@ TREC Deep Learning 2022. arXiv preprint arXiv:2302.12574 (2023). [30] Patrick Lewis, Pontus Stenetorp, and Sebastian Riedel. 2021. Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 1000–1008. [31] Wenhao Lu, Jian Jiao, and Ruofei Zhang. 2020. Twinbert: Distilling knowledge to twin-structured compressed BERT models for large-scale retrieval. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2645–2652. [32] microsoft. 0. Bing search. https://www.bing.com/. [33] microsoft. 0. New Bing. https://www.bing.com/new. [34] Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. 2021. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332 (2021). [35] Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In CoCo@ NIPS. [36] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35 (2022), 27730–27744. [37] Arnold Overwijk, Chenyan Xiong, and Jamie Callan. 2022. ClueWeb22: 10 billion web documents with rich information. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 3360–3362. [38] Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward. 2016. Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval. IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, 4 (2016), 694–707. [39] Yifan Qiao, Chenyan Xiong, Zhenghao Liu, and Zhiyuan Liu. 2019. Understanding the Behaviors of BERT in Ranking. arXiv preprint arXiv:1904.07531 (2019). [40] Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019). [41] Jie Ren, Minjia Zhang, and Dong Li. 2020. HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory. In In Proceedings of the 34th International Conference on Neural Information Processing Systems, Vol. 33. [42] Stephen E Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR’94: Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, organised by Dublin City University. Springer, 232–241. [43] Shota Sasaki, Shuo Sun, Shigehiko Schamoni, Kevin Duh, and Kentaro Inui. 2018. Cross-lingual learning-to-rank with shared representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 458–463. [44] Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur Parikh, Ali Farhadi, and Hannaneh Hajishirzi. 2019. Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 4430–4441. [45] Xuan Shan, Chuanjie Liu, Yiqian Xia, Qi Chen, Yusi Zhang, Kaize Ding, Yaobo Liang, Angen Luo, and Yuxiang Luo. 2021. GLOW: Global Weighted SelfAttention Network for Web Search. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 519–528. [46] Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014. Learning semantic representations using convolutional neural networks for web search. In Proceedings of the 23rd international conference on world wide web. 373–374. [47] Ian Soboroff. 2021. Overview of TREC 2021. In 30th Text REtrieval Conference. Gaithersburg, Maryland. [48] Suhas Jayaram Subramanya, Rohan Kadekodi, Ravishankar Krishaswamy, and Harsha Vardhan Simhadri. 2019. Diskann: Fast accurate billion-point nearest neighbor search on a single node. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 13766–13776. [49] Yi Tay, Vinh Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, et al. 2022. Transformer memory as a differentiable search index. Advances in Neural Information Processing Systems 35 (2022), 21831–21843. [50] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023). [51] Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Qi Chen, Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, et al. 2022. A neural corpus indexer for document retrieval. Advances in Neural Information Processing Systems 35 (2022), 25600–25614. [52] Shitao Xiao, Zheng Liu, Weihao Han, Jianjin Zhang, Defu Lian, Yeyun Gong, Qi Chen, Fan Yang, Hao Sun, Yingxia Shao, et al. 2022. Distill-vq: Learning retrieval oriented vector quantization by distilling knowledge from dense embeddings. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1513–1523. [53] Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighof. 2023. C-pack: Packaged resources to advance general chinese embedding. arXiv preprint arXiv:2309.07597 (2023). [54] Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk. 2020. Approximate nearest neighbor negative contrastive learning for dense text retrieval. arXiv preprint arXiv:2007.00808 (2020). [55] Linlong Xu, Baosong Yang, Xiaoyu Lv, Tianchi Bi, Dayiheng Liu, and Haibo Zhang. 2021. Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval. arXiv preprint arXiv:2111.01992 (2021). [56] Jingtao Zhan, Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, and Shaoping Ma. 2022. Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2486–2496. [57] Kun Zhou, Yeyun Gong, Xiao Liu, Wayne Xin Zhao, Yelong Shen, Anlei Dong, Jingwen Lu, Rangan Majumder, Ji-Rong Wen, and Nan Duan. 2022. SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track. 548–559. [58] Shengyao Zhuang, Hang Li, and G. Zuccon. 2021. Deep Query Likelihood Model for Information Retrieval. In ECIR. Authors:
(1) Qi Chen, Microsoft Beijing, China;
(2) Xiubo Geng, Microsoft Beijing, China;
(3) Corby Rosset, Microsoft, Redmond, United States;
(4) Carolyn Buractaon, Microsoft, Redmond, United States;
(5) Jingwen Lu, Microsoft, Redmond, United States;
(6) Tao Shen, University of Technology Sydney, Sydney, Australia and the work was done at Microsoft;
(7) Kun Zhou, Microsoft, Beijing, China;
(8) Chenyan Xiong, Carnegie Mellon University, Pittsburgh, United States and the work was done at Microsoft;
(9) Yeyun Gong, Microsoft, Beijing, China;
(10) Paul Bennett, Spotify, New York, United States and the work was done at Microsoft;
(11) Nick Craswell, Microsoft, Redmond, United States;
(12) Xing Xie, Microsoft, Beijing, China;
(13) Fan Yang, Microsoft, Beijing, China;
(14) Bryan Tower, Microsoft, Redmond, United States;
(15) Nikhil Rao, Microsoft, Mountain View, United States;
(16) Anlei Dong, Microsoft, Mountain View, United States;
(17) Wenqi Jiang, ETH Zürich, Zürich, Switzerland;
(18) Zheng Liu, Microsoft, Beijing, China;
(19) Mingqin Li, Microsoft, Redmond, United States;
(20) Chuanjie Liu, Microsoft, Beijing, China;
(21) Zengzhong Li, Microsoft, Redmond, United States;
(22) Rangan Majumder, Microsoft, Redmond, United States;
(23) Jennifer Neville, Microsoft, Redmond, United States;
(24) Andy Oakley, Microsoft, Redmond, United States;
(25) Knut Magne Risvik, Microsoft, Oslo, Norway;
(26) Harsha Vardhan Simhadri, Microsoft, Bengaluru, India;
(27) Manik Varma, Microsoft, Bengaluru, India;
(28) Yujing Wang, Microsoft, Beijing, China;
(29) Linjun Yang, Microsoft, Redmond, United States;
(30) Mao Yang, Microsoft, Beijing, China;
(31) Ce Zhang, ETH Zürich, Zürich, Switzerland and the work was done at Microsoft. Authors: Authors: (1) Qi Chen, Microsoft Beijing, China; (2) Xiubo Geng, Microsoft Beijing, China; (3) Corby Rosset, Microsoft, Redmond, United States; (4) Carolyn Buractaon, Microsoft, Redmond, United States; (5) Jingwen Lu, Microsoft, Redmond, United States; (6) Tao Shen, University of Technology Sydney, Sydney, Australia and the work was done at Microsoft; (7) Kun Zhou, Microsoft, Beijing, China; (8) Chenyan Xiong, Carnegie Mellon University, Pittsburgh, United States and the work was done at Microsoft; (9) Yeyun Gong, Microsoft, Beijing, China; (10) Paul Bennett, Spotify, New York, United States and the work was done at Microsoft; (11) Nick Craswell, Microsoft, Redmond, United States; (12) Xing Xie, Microsoft, Beijing, China; (13) Fan Yang, Microsoft, Beijing, China; (14) Bryan Tower, Microsoft, Redmond, United States; (15) Nikhil Rao, Microsoft, Mountain View, United States; (16) Anlei Dong, Microsoft, Mountain View, United States; (17) Wenqi Jiang, ETH Zürich, Zürich, Switzerland; (18) Zheng Liu, Microsoft, Beijing, China; (19) Mingqin Li, Microsoft, Redmond, United States; (20) Chuanjie Liu, Microsoft, Beijing, China; (21) Zengzhong Li, Microsoft, Redmond, United States; (22) Rangan Majumder, Microsoft, Redmond, United States; (23) Jennifer Neville, Microsoft, Redmond, United States; (24) Andy Oakley, Microsoft, Redmond, United States; (25) Knut Magne Risvik, Microsoft, Oslo, Norway; (26) Harsha Vardhan Simhadri, Microsoft, Bengaluru, India; (27) Manik Varma, Microsoft, Bengaluru, India; (28) Yujing Wang, Microsoft, Beijing, China; (29) Linjun Yang, Microsoft, Redmond, United States; (30) Mao Yang, Microsoft, Beijing, China; (31) Ce Zhang, ETH Zürich, Zürich, Switzerland and the work was done at Microsoft. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Quant

Empirical

Microsoft

Navigating Skew: Addressing Language & Domain Biases in Web Data

REPO of Open Technology Datasets

Too Long; Didn't Read

Make resilience your competitive advantage

References for Web-Scale Information Retrieval Challenges

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

Navigating Skew: Addressing Language & Domain Biases in Web Data

MS MARCO Web Search: Powering Next-Gen Information Access & Neural Indexers

Challenges in Web-Scale Information Retrieval: From Keywords to Embeddings

Why New Datasets are Needed for Deep Learning-Enhanced IR

Introducing MS MARCO Web Search: A New Era for LLM and IR Data

Navigating Skew: Addressing Language & Domain Biases in Web Data

MS MARCO Web Search: Powering Next-Gen Information Access & Neural Indexers

Challenges in Web-Scale Information Retrieval: From Keywords to Embeddings

Why New Datasets are Needed for Deep Learning-Enhanced IR

Introducing MS MARCO Web Search: A New Era for LLM and IR Data

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps