@inproceedings{luo-etal-2023-study,
title = "A Study on the Efficiency and Generalization of Light Hybrid Retrievers",
author = "Luo, Man and
Jain, Shashank and
Gupta, Anchit and
Einolghozati, Arash and
Oguz, Barlas and
Chatterjee, Debojeet and
Chen, Xilun and
Baral, Chitta and
Heidari, Peyman",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.139",
doi = "10.18653/v1/2023.acl-short.139",
pages = "1617--1626",
abstract = "Hybrid retrievers can take advantage of both sparse and dense retrievers. Previous hybrid retrievers leverage indexing-heavy dense retrievers. In this work, we study {``}Is it possible to reduce the indexing memory of hybrid retrievers without sacrificing performance{''}? Driven by this question, we leverage an indexing-efficient dense retriever (i.e. DrBoost) and introduce a LITE retriever that further reduces the memory of DrBoost. LITE is jointly trained on contrastive learning and knowledge distillation from DrBoost. Then, we integrate BM25, a sparse retriever, with either LITE or DrBoost to form light hybrid retrievers. Our Hybrid-LITE retriever saves $13\times$ memory while maintaining 98.0{\%} performance of the hybrid retriever of BM25 and DPR. In addition, we study the generalization capacity of our light hybrid retrievers on out-of-domain dataset and a set of adversarial attacks datasets. Experiments showcase that light hybrid retrievers achieve better generalization performance than individual sparse and dense retrievers. Nevertheless, our analysis shows that there is a large room to improve the robustness of retrievers, suggesting a new research direction.",
}
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<abstract>Hybrid retrievers can take advantage of both sparse and dense retrievers. Previous hybrid retrievers leverage indexing-heavy dense retrievers. In this work, we study “Is it possible to reduce the indexing memory of hybrid retrievers without sacrificing performance”? Driven by this question, we leverage an indexing-efficient dense retriever (i.e. DrBoost) and introduce a LITE retriever that further reduces the memory of DrBoost. LITE is jointly trained on contrastive learning and knowledge distillation from DrBoost. Then, we integrate BM25, a sparse retriever, with either LITE or DrBoost to form light hybrid retrievers. Our Hybrid-LITE retriever saves 13\times memory while maintaining 98.0% performance of the hybrid retriever of BM25 and DPR. In addition, we study the generalization capacity of our light hybrid retrievers on out-of-domain dataset and a set of adversarial attacks datasets. Experiments showcase that light hybrid retrievers achieve better generalization performance than individual sparse and dense retrievers. Nevertheless, our analysis shows that there is a large room to improve the robustness of retrievers, suggesting a new research direction.</abstract>
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%0 Conference Proceedings
%T A Study on the Efficiency and Generalization of Light Hybrid Retrievers
%A Luo, Man
%A Jain, Shashank
%A Gupta, Anchit
%A Einolghozati, Arash
%A Oguz, Barlas
%A Chatterjee, Debojeet
%A Chen, Xilun
%A Baral, Chitta
%A Heidari, Peyman
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F luo-etal-2023-study
%X Hybrid retrievers can take advantage of both sparse and dense retrievers. Previous hybrid retrievers leverage indexing-heavy dense retrievers. In this work, we study “Is it possible to reduce the indexing memory of hybrid retrievers without sacrificing performance”? Driven by this question, we leverage an indexing-efficient dense retriever (i.e. DrBoost) and introduce a LITE retriever that further reduces the memory of DrBoost. LITE is jointly trained on contrastive learning and knowledge distillation from DrBoost. Then, we integrate BM25, a sparse retriever, with either LITE or DrBoost to form light hybrid retrievers. Our Hybrid-LITE retriever saves 13\times memory while maintaining 98.0% performance of the hybrid retriever of BM25 and DPR. In addition, we study the generalization capacity of our light hybrid retrievers on out-of-domain dataset and a set of adversarial attacks datasets. Experiments showcase that light hybrid retrievers achieve better generalization performance than individual sparse and dense retrievers. Nevertheless, our analysis shows that there is a large room to improve the robustness of retrievers, suggesting a new research direction.
%R 10.18653/v1/2023.acl-short.139
%U https://aclanthology.org/2023.acl-short.139
%U https://doi.org/10.18653/v1/2023.acl-short.139
%P 1617-1626
Markdown (Informal)
[A Study on the Efficiency and Generalization of Light Hybrid Retrievers](https://aclanthology.org/2023.acl-short.139) (Luo et al., ACL 2023)
ACL
- Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral, and Peyman Heidari. 2023. A Study on the Efficiency and Generalization of Light Hybrid Retrievers. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1617–1626, Toronto, Canada. Association for Computational Linguistics.