@inproceedings{kim-etal-2026-qudar,
title = "{Q}u{DAR}: Query-Wise Dual-Perspective Adaptive Retrieval",
author = "Kim, Joeun and
Yoon, Seunghyouk and
Le, Xuan-Bach and
Nam, Youngeun and
Kim, Doyoung and
Song, Hwanjun and
Lee, Jae-Gil",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1791/",
pages = "38662--38679",
ISBN = "979-8-89176-390-6",
abstract = "Retrieval-augmented generation(RAG) systems depend on retrieval modules to supply grounding evidence for large language models. While hybrid approaches combining sparse and dense retrievers improve performance, most rely on fixed weights that ignore query-specific and corpus-specific variation. Similarly, query expansion has long been used to enrich recall, but its integration with original queries is usually static and can introduce noise. We present QuDAR, a dual-perspective adaptive retrieval framework that adapts along two perspectives: retriever type (sparse vs. dense) and query format (original vs.expanded). Leveraging margin-derived confidence (e.g., top-1{--}top-2 score gaps) and blind LLM-based relevance scoring, QuDAR dynamically assigns query-specific weights, fusing lexical specificity with semantic breadth while mitigating noise. QuDAR is lightweight, retriever-agnostic, and broadly applicable. Experiments show consistent gains over static baselines, improving overall retrieval quality and yielding more stable performance across queries."
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%0 Conference Proceedings
%T QuDAR: Query-Wise Dual-Perspective Adaptive Retrieval
%A Kim, Joeun
%A Yoon, Seunghyouk
%A Le, Xuan-Bach
%A Nam, Youngeun
%A Kim, Doyoung
%A Song, Hwanjun
%A Lee, Jae-Gil
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F kim-etal-2026-qudar
%X Retrieval-augmented generation(RAG) systems depend on retrieval modules to supply grounding evidence for large language models. While hybrid approaches combining sparse and dense retrievers improve performance, most rely on fixed weights that ignore query-specific and corpus-specific variation. Similarly, query expansion has long been used to enrich recall, but its integration with original queries is usually static and can introduce noise. We present QuDAR, a dual-perspective adaptive retrieval framework that adapts along two perspectives: retriever type (sparse vs. dense) and query format (original vs.expanded). Leveraging margin-derived confidence (e.g., top-1–top-2 score gaps) and blind LLM-based relevance scoring, QuDAR dynamically assigns query-specific weights, fusing lexical specificity with semantic breadth while mitigating noise. QuDAR is lightweight, retriever-agnostic, and broadly applicable. Experiments show consistent gains over static baselines, improving overall retrieval quality and yielding more stable performance across queries.
%U https://aclanthology.org/2026.acl-long.1791/
%P 38662-38679
Markdown (Informal)
[QuDAR: Query-Wise Dual-Perspective Adaptive Retrieval](https://aclanthology.org/2026.acl-long.1791/) (Kim et al., ACL 2026)
ACL
- Joeun Kim, Seunghyouk Yoon, Xuan-Bach Le, Youngeun Nam, Doyoung Kim, Hwanjun Song, and Jae-Gil Lee. 2026. QuDAR: Query-Wise Dual-Perspective Adaptive Retrieval. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 38662–38679, San Diego, California, United States. Association for Computational Linguistics.