Query Encoder Distillation via Embedding Alignment is a Strong Baseline Method to Boost Dense Retriever Online Efficiency
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Abstract (arXiv)
The information retrieval community has made significant progress in improving the efficiency of Dual Encoder (DE) dense passage retrieval systems, making them suitable for latency-sensitive settings. However, many proposed procedures are often too complex or resource-intensive, which makes it difficult for practitioners to adopt them or identify sources of empirical gains. Therefore, in this work, we propose a trivially simple recipe to serve as a baseline method for boosting the efficiency of DE retrievers leveraging an asymmetric architecture. Our results demonstrate that even a 2-layer, BERT-based query encoder can still retain 92.5% of the full DE performance on the BEIR benchmark via unsupervised distillation and proper student initialization. We hope that our findings will encourage the community to re-evaluate the trade-offs between method complexity and performance improvements.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 36 of its 55 references that are in the map. First posted on arXiv 5 Jun 2023.
Builds on
36 of 55 references.
References · 36
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24 more
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Built on by
Citing papers per year: 2025: 1; 2026: 4.
5 papers.
- 2026NanoVDR: Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder for Visual Document Retrievalcited by 2
- 2025LightRetriever: A LLM-based Text Retrieval Architecture with Extremely Faster Query Inferencecited by 1
- 2026Succeeding at Scale: Enterprise Retrieval Benchmark Construction and Index-Preserving Query Adaptation for Multi-Tenant Searchcited by 0
- 2026Mixture-of-Experts Language Models Can Be Strong and Efficient Retrieverscited by 0
- 2026ColNanoVDR: Document-Free Query Distillation for Multi-Vector Visual Document Retrieval via Optimal Transportcited by 0
- 2026ColNanoVDR: Document-Free Query Distillation for Multi-Vector Visual Document Retrieval via Optimal Transport28 Sep 2026
- 2026Mixture-of-Experts Language Models Can Be Strong and Efficient Retrievers11 Sep 2026
- 2026NanoVDR: Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder for Visual Document Retrieval13 Mar 2026
- 2026Succeeding at Scale: Enterprise Retrieval Benchmark Construction and Index-Preserving Query Adaptation for Multi-Tenant Search8 Jan 2026
- 2025LightRetriever: A LLM-based Text Retrieval Architecture with Extremely Faster Query Inference18 May 2025