@inproceedings{kim-etal-2025-query,
title = "Query-focused Referentiability Learning for Zero-shot Retrieval",
author = "Kim, Jaeyoung and
Lee, Dohyeon and
Hwang, Seung-won",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.276/",
doi = "10.18653/v1/2025.naacl-long.276",
pages = "5345--5358",
ISBN = "979-8-89176-189-6",
abstract = "Dense passage retrieval enhances Information Retrieval (IR) by encoding queries and passages into representation space. However, passage representations often fail to be referenced by their gold queries under domain shifts, revealing a weakness in representation space. One desirable concept for representations is ``argmaxable''. Being argmaxable ensures that no representations are theoretically excluded from selection due to geometric constraints. To be argmaxable, a notable approach is to increase isotropy, where representations are evenly spread out in all directions. These findings, while desirable also for IR, focus on passage representation and not on query, making it challenging to directly apply their findings to IR. In contrast, we introduce a novel query-focused concept of ``referentiable'' tailored for IR tasks, which ensures that passage representations are referenced by their gold queries. Building on this, we propose Learning Referentiable Representation (LRR), and two strategic metrics, Self-P and Self-Q, quantifying how the representations are referentiable. Our experiments compare three dense model versions: Naive, Isotropic, and Referentiable, demonstrating that LRR leads to enhanced zero-shot performance, surpassing existing naive and isotropic versions."
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<abstract>Dense passage retrieval enhances Information Retrieval (IR) by encoding queries and passages into representation space. However, passage representations often fail to be referenced by their gold queries under domain shifts, revealing a weakness in representation space. One desirable concept for representations is “argmaxable”. Being argmaxable ensures that no representations are theoretically excluded from selection due to geometric constraints. To be argmaxable, a notable approach is to increase isotropy, where representations are evenly spread out in all directions. These findings, while desirable also for IR, focus on passage representation and not on query, making it challenging to directly apply their findings to IR. In contrast, we introduce a novel query-focused concept of “referentiable” tailored for IR tasks, which ensures that passage representations are referenced by their gold queries. Building on this, we propose Learning Referentiable Representation (LRR), and two strategic metrics, Self-P and Self-Q, quantifying how the representations are referentiable. Our experiments compare three dense model versions: Naive, Isotropic, and Referentiable, demonstrating that LRR leads to enhanced zero-shot performance, surpassing existing naive and isotropic versions.</abstract>
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%0 Conference Proceedings
%T Query-focused Referentiability Learning for Zero-shot Retrieval
%A Kim, Jaeyoung
%A Lee, Dohyeon
%A Hwang, Seung-won
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F kim-etal-2025-query
%X Dense passage retrieval enhances Information Retrieval (IR) by encoding queries and passages into representation space. However, passage representations often fail to be referenced by their gold queries under domain shifts, revealing a weakness in representation space. One desirable concept for representations is “argmaxable”. Being argmaxable ensures that no representations are theoretically excluded from selection due to geometric constraints. To be argmaxable, a notable approach is to increase isotropy, where representations are evenly spread out in all directions. These findings, while desirable also for IR, focus on passage representation and not on query, making it challenging to directly apply their findings to IR. In contrast, we introduce a novel query-focused concept of “referentiable” tailored for IR tasks, which ensures that passage representations are referenced by their gold queries. Building on this, we propose Learning Referentiable Representation (LRR), and two strategic metrics, Self-P and Self-Q, quantifying how the representations are referentiable. Our experiments compare three dense model versions: Naive, Isotropic, and Referentiable, demonstrating that LRR leads to enhanced zero-shot performance, surpassing existing naive and isotropic versions.
%R 10.18653/v1/2025.naacl-long.276
%U https://aclanthology.org/2025.naacl-long.276/
%U https://doi.org/10.18653/v1/2025.naacl-long.276
%P 5345-5358
Markdown (Informal)
[Query-focused Referentiability Learning for Zero-shot Retrieval](https://aclanthology.org/2025.naacl-long.276/) (Kim et al., NAACL 2025)
ACL
- Jaeyoung Kim, Dohyeon Lee, and Seung-won Hwang. 2025. Query-focused Referentiability Learning for Zero-shot Retrieval. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5345–5358, Albuquerque, New Mexico. Association for Computational Linguistics.