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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.

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Cited By

Siddiq MZhang JSantos JBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)Understanding Regular Expression Denial of Service (ReDoS): Insights from LLM-Generated Regexes and Developer ForumsProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644424(190-201)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643916.3644424
Ma XWang LYang NWei FLin JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Fine-Tuning LLaMA for Multi-Stage Text RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657951(2421-2425)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657951
G BR PV VN AFaheem MT SK K(2024)Comparative Evaluation of Large Language Models for Abstractive Summarization2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence60223.2024.10463521(59-64)Online publication date: 18-Jan-2024
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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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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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Siddiq MZhang JSantos JBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)Understanding Regular Expression Denial of Service (ReDoS): Insights from LLM-Generated Regexes and Developer ForumsProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644424(190-201)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643916.3644424
Ma XWang LYang NWei FLin JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Fine-Tuning LLaMA for Multi-Stage Text RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657951(2421-2425)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657951
G BR PV VN AFaheem MT SK K(2024)Comparative Evaluation of Large Language Models for Abstractive Summarization2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence60223.2024.10463521(59-64)Online publication date: 18-Jan-2024
https://doi.org/10.1109/Confluence60223.2024.10463521
N AB ST SG BDoss S(2024)Comparative Analysis of News Articles Summarization using LLMs2024 Asia Pacific Conference on Innovation in Technology (APCIT)10.1109/APCIT62007.2024.10673458(1-6)Online publication date: 26-Jul-2024
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Balakrishnan RPati PSingh RS SKumar P(2024)Fine-Tuned T5 For Auto-Grading Of Quadratic Equation ProblemsProcedia Computer Science10.1016/j.procs.2024.04.206235(2178-2186)Online publication date: 2024
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Fan YHuang CRen THe ZWang XWu XWang YLi JYang Y(2024)Gar $$\scriptstyle ++$$: Natural Language to SQL Translation with Efficient Generate-and-RankWeb and Big Data10.1007/978-981-97-7238-4_26(411-427)Online publication date: 28-Aug-2024
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Ampazis N(2024)Improving RAG Quality for Large Language Models with Topic-Enhanced RerankingArtificial Intelligence Applications and Innovations10.1007/978-3-031-63215-0_6(74-87)Online publication date: 19-Jun-2024
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Rau DKamps J(2024)Query Generation Using Large Language ModelsAdvances in Information Retrieval10.1007/978-3-031-56066-8_19(226-239)Online publication date: 24-Mar-2024
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Petrov AMacAvaney SMacdonald C(2024)Shallow Cross-Encoders for Low-Latency RetrievalAdvances in Information Retrieval10.1007/978-3-031-56063-7_10(151-166)Online publication date: 24-Mar-2024
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Baldelli DJiang JAizawa ATorroni P(2024)TWOLAR: A TWO-Step LLM-Augmented Distillation Method for Passage RerankingAdvances in Information Retrieval10.1007/978-3-031-56027-9_29(470-485)Online publication date: 24-Mar-2024
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