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RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses

Authors:

Honglei Zhuang,

Michael BenderskyAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2308 - 2313

https://doi.org/10.1145/3539618.3592047

Published: 18 July 2023 Publication History

Abstract

Pretrained language models such as BERT have been shown to be exceptionally effective for text ranking. However, there are limited studies on how to leverage more powerful sequence-to-sequence models such as T5. Existing attempts usually formulate text ranking as a classification problem and rely on postprocessing to obtain a ranked list. In this paper, we propose RankT5 and study two T5-based ranking model structures, an encoder-decoder and an encoder-only one, so that they not only can directly output ranking scores for each query-document pair, but also can be fine-tuned with pairwise or listwise ranking losses to optimize ranking performance. Our experiments show that the proposed models with ranking losses can achieve substantial ranking performance gains on different public text ranking data sets. Moreover, ranking models fine-tuned with listwise ranking losses have better zero-shot ranking performance on out-of-domain data than models fine-tuned with classification losses.

References

[1]

Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al. 2016. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. arXiv preprint arXiv:1611.09268 (2016).

[2]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, Vol. 33. 1877--1901.

[3]

Sebastian Bruch, Xuanhui Wang, Michael Bendersky, and Marc Najork. 2019. An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance. In Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval. 75--78.

Digital Library

[4]

Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd International Conference on Machine learning. 89--96.

Digital Library

[5]

Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to Rank: From Pairwise Approach to Listwise Approach. In Proceedings of the 24th International Conference on Machine Learning. 129--136.

Digital Library

[6]

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022. PaLM: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311 (2022).

[7]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171--4186.

[8]

Cicero dos Santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, and Bing Xiang. 2020. Beyond [CLS] through Ranking by Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. 1722--1727.

[9]

Luyu Gao, Zhuyun Dai, and Jamie Callan. 2020. Modularized Transfomer-based Ranking Framework. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. 4180--4190.

[10]

Luyu Gao, Zhuyun Dai, and Jamie Callan. 2021a. Rethink training of BERT rerankers in multi-stage retrieval pipeline. In European Conference on Information Retrieval. Springer, 280--286.

Digital Library

[11]

Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021b. SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 6894--6910.

[12]

Shuguang Han, Xuanhui Wang, Mike Bendersky, and Marc Najork. 2020. Learning-to-Rank with BERT in TF-Ranking. arXiv preprint arXiv:2004.08476 (2020).

[13]

Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Prakash Gupta, Cicero Nogueira dos Santos, Yi Tay, and Don Metzler. 2022. ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference. In Findings of the Association for Computational Linguistics: ACL 2022. 3747--3758.

[14]

Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky, and Marc Najork. 2022a. On Optimizing Top-K Metrics for Neural Ranking Models. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2303--2307.

Digital Library

[15]

Rolf Jagerman, Xuanhui Wang, Honglei Zhuang, Zhen Qin, Michael Bendersky, and Marc Najork. 2022b. Rax: Composable Learning-to-Rank using JAX. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3051--3060.

Digital Library

[16]

Kalervo Jrveliän and Jaana Kekäläinen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, Vol. 20, 4 (2002), 422--446.

Digital Library

[17]

Jia-Huei Ju, Jheng-Hong Yang, and Chuan-Ju Wang. 2021. Text-to-text Multi-view Learning for Passage Re-ranking. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1803--1807.

Digital Library

[18]

Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. 6769--6781.

[19]

Omar Khattab and Matei Zaharia. 2020. Col-BERT: Efficient and Effective Passage Search via Contextualized late Interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 39--48.

Digital Library

[20]

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, Vol. 7 (2019), 453--466.

[21]

John Lafferty and Chengxiang Zhai. 2003. Probabilistic relevance models based on document and query generation. In Language modeling for information retrieval. Springer, 1--10.

[22]

Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Jay Shi, Shuyang Cheng, and Dragomir Anguelov. 2022. PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions. In International Conference on Learning Representations.

[23]

Jimmy Lin, Rodrigo Nogueira, and Andrew Yates. 2021. Pretrained transformers for text ranking: BERT and beyond. Morgan & Claypool Publishers.

[24]

Binsheng Liu, Hamed Zamani, Xiaolu Lu, and J Shane Culpepper. 2021. Generalizing discriminative retrieval models using generative tasks. In Proceedings of the Web Conference 2021. 3745--3756.

Digital Library

[25]

Tie-Yan Liu. 2009. Learning to Rank for Information Retrieval. Now Publishers Inc.

Digital Library

[26]

Jing Lu, Gustavo Hernández Ábrego, Ji Ma, Jianmo Ni, and Yinfei Yang. 2021. Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 6091--6103.

[27]

Yuxiang Lu, Yiding Liu, Jiaxiang Liu, Yunsheng Shi, Zhengjie Huang, Shikun Feng Yu Sun, Hao Tian, Hua Wu, Shuaiqiang Wang, Dawei Yin, et al. 2022. ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval. arXiv preprint arXiv:2205.09153 (2022).

[28]

Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, and Ophir Frieder. 2020. Efficient document re-ranking for transformers by precomputing term representations. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 49--58.

Digital Library

[29]

Sean MacAvaney, Nicola Tonellotto, and Craig Macdonald. 2022. Adaptive re-ranking with a corpus graph. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1491--1500.

Digital Library

[30]

Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith Hall, Daniel Cer, and Yinfei Yang. 2022a. Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. In Findings of the Association for Computational Linguistics: ACL 2022. 1864--1874.

[31]

Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y Zhao, Yi Luan, Keith B Hall, Ming-Wei Chang, et al. 2022b. Large dual encoders are generalizable retrievers. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.

[32]

Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document Ranking with a Pretrained Sequence-to-Sequence Model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. 708--718.

[33]

Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019. Multi-stage document ranking with BERT. arXiv preprint arXiv:1910.14424 (2019).

[34]

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Gray, et al. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems.

[35]

Ronak Pradeep, Rodrigo Nogueira, and Jimmy Lin. 2021. The expando-mono-duo design pattern for text ranking with pretrained sequence-to-sequence models. arXiv preprint arXiv:2101.05667 (2021).

[36]

Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, and Marc Najork. 2021. Are Neural Rankers Still Outperformed by Gradient Boosted Decision Trees?. In International Conference on Learning Representations.

[37]

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, Vol. 21 (2020), 1--67.

[38]

Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, Qiaoqiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2021. RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2825--2835.

[39]

Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.

[40]

Devendra Singh Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen tau Yih, Joëlle Pineau, and Luke Zettlemoyer. 2022. Improving Passage Retrieval with Zero-Shot Question Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.

[41]

Yi Tay, Vinh Q 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. In Advances in Neural Information Processing Systems.

[42]

Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021. BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).

[43]

Ellen M Voorhees. 1999. The TREC-8 Question Answering Track Report. In Proceedings of the 8th Text REtrieval Conference (TREC-8), Vol. 99. 77--82.

[44]

Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022. SimLM: Pre-training with representation bottleneck for dense passage retrieval. arXiv preprint arXiv:2207.02578 (2022).

[45]

Shitao Xiao and Zheng Liu. 2022. RetroMAE v2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models. arXiv preprint arXiv:2211.08769 (2022).

[46]

Shitao Xiao, Zheng Liu, Yingxia Shao, and Zhao Cao. 2022. RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 538--548.

[47]

Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N Bennett, Junaid Ahmed, and Arnold Overwijk. 2020. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In International Conference on Learning Representations.

[48]

Yanzhao Zhang, Dingkun Long, Guangwei Xu, and Pengjun Xie. 2022. HLATR: enhance multi-stage text retrieval with hybrid list aware transformer reranking. arXiv preprint arXiv:2205.10569 (2022).

[49]

Shengyao Zhuang, Hang Li, and Guido Zuccon. 2021. Deep Query Likelihood Model for Information Retrieval. In European Conference On Information Retrieval. Springer, 463--470.

[50]

Shengyao Zhuang and Guido Zuccon. 2021. TILDE: Term Independent Likelihood moDEl for Passage Re-ranking. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1483--1492.

Digital Library

Cited By

Guo FLi WZhuang HLuo YLi YYan LZhu QZhang YNejdl WAuer SKarras OCha MMoens MNajork M(2025)MCRanker: Generating Diverse Criteria On-the-Fly to Improve Pointwise LLM RankersProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703583(944-953)Online publication date: 10-Mar-2025
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Dadas SGrȩbowiec M(2025)Assessing Generalization Capability of Text Ranking Models in PolishArtificial Intelligence and Soft Computing10.1007/978-3-031-84353-2_4(37-49)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-3-031-84353-2_4
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Index Terms

RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

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Information & Contributors

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Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 18 July 2023

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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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https://dl.acm.org/doi/10.1145/3701551.3703583
Zhang YHuang BYuan WJiang ZPeng LChen STan-Soo J(2025)Expert-level policy style measurement via knowledge distillation with large language model collaborationInformation Processing & Management10.1016/j.ipm.2025.10409062:4(104090)Online publication date: Jul-2025
https://doi.org/10.1016/j.ipm.2025.104090
Dadas SGrȩbowiec M(2025)Assessing Generalization Capability of Text Ranking Models in PolishArtificial Intelligence and Soft Computing10.1007/978-3-031-84353-2_4(37-49)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-3-031-84353-2_4
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