Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder
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Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.
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Dense retrieval baselines generally perform better than sparse retrieval baselines on MS300K.
“The performance of dense retrieval baselines is generally better than that of sparse retrieval baselines, likely because the former uses dense vectors to capture richer semantic information, which is also consistent with the findings [31,32].”
From Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval (2025) · cites this paper as [31]
Section 4.3 and Table 2 compare dense and sparse retrieval baselines on MS MARCO; several dense models outperform the sparse baselines. The paper does not report results on MS300K, so it supports the general comparison but not the claim for that dataset. (LLM, conf=0.85)
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Cited by
Sorted by citations
Precise Zero-Shot Dense Retrieval without Relevance Labels
Gao, L., Ma, X., Lin, J., & Callan, J. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.99 · Cited by 362 · see citing works
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Self-citationLightweight and Direct Document Relevance Optimization for Generative Information Retrieval
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“From Table 4, we can find: (i) On the MS MARCO dataset, removing pair-wise ranking optimization significantly reduces intermediate and broader recall metrics (R@5 and R@10) for both DDRO variants, highlighting its critical role in improving retrieval performance.”
- Authors
- Lu, S., He, D., Xiong, C., Ke, G., Malik, W., Dou, Z., Bennett, P., Liu, T.-Y., & Overwijk, A.
- Affiliations
- Renmin University of China (CN)
- Microsoft (United States) (US)
- ORCID
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 2780-2791
- OpenAlex
- W3212725701
- Cited by
- 49
- References
- 12
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 5 · 2023: 22 · 2022: 12
- FWCI
- 4.50
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (61872370)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- Siamese encoder, contrastive learning, retrieval-augmented generation
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