@inproceedings{yu-etal-2025-ca,
title = "{CA}-{GAR}: Context-Aware Alignment of {LLM} Generation for Document Retrieval",
author = "Yu, Heng and
Kang, Junfeng and
Li, Rui and
Liu, Qi and
He, Liyang and
Huang, Zhenya and
Shen, Shuanghong and
Lu, Junyu",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.303/",
doi = "10.18653/v1/2025.findings-acl.303",
pages = "5836--5849",
ISBN = "979-8-89176-256-5",
abstract = "Information retrieval has evolved from traditional sparse and dense retrieval methods to approaches driven by large language models (LLMs). Recent techniques, such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieval (GDR), leverage LLMs to enhance retrieval but face key challenges: GAR{'}s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLMs by constraining outputs to predefined document identifiers. To address these issues, we propose Context-Aware Generation-Augmented Retrieval (CA-GAR), which enhances LLMs by integrating corpus information into their generation process. CA-GAR optimizes token selection by incorporating relevant document information and leverages a Distribution Alignment Strategy to extract corpus information using a lexicon-based approach. Experimental evaluations on seven tasks from the BEIR benchmark and four non-English languages from Mr.TyDi demonstrate that CA-GAR outperforms existing methods."
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<abstract>Information retrieval has evolved from traditional sparse and dense retrieval methods to approaches driven by large language models (LLMs). Recent techniques, such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieval (GDR), leverage LLMs to enhance retrieval but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLMs by constraining outputs to predefined document identifiers. To address these issues, we propose Context-Aware Generation-Augmented Retrieval (CA-GAR), which enhances LLMs by integrating corpus information into their generation process. CA-GAR optimizes token selection by incorporating relevant document information and leverages a Distribution Alignment Strategy to extract corpus information using a lexicon-based approach. Experimental evaluations on seven tasks from the BEIR benchmark and four non-English languages from Mr.TyDi demonstrate that CA-GAR outperforms existing methods.</abstract>
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%0 Conference Proceedings
%T CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval
%A Yu, Heng
%A Kang, Junfeng
%A Li, Rui
%A Liu, Qi
%A He, Liyang
%A Huang, Zhenya
%A Shen, Shuanghong
%A Lu, Junyu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F yu-etal-2025-ca
%X Information retrieval has evolved from traditional sparse and dense retrieval methods to approaches driven by large language models (LLMs). Recent techniques, such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieval (GDR), leverage LLMs to enhance retrieval but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLMs by constraining outputs to predefined document identifiers. To address these issues, we propose Context-Aware Generation-Augmented Retrieval (CA-GAR), which enhances LLMs by integrating corpus information into their generation process. CA-GAR optimizes token selection by incorporating relevant document information and leverages a Distribution Alignment Strategy to extract corpus information using a lexicon-based approach. Experimental evaluations on seven tasks from the BEIR benchmark and four non-English languages from Mr.TyDi demonstrate that CA-GAR outperforms existing methods.
%R 10.18653/v1/2025.findings-acl.303
%U https://aclanthology.org/2025.findings-acl.303/
%U https://doi.org/10.18653/v1/2025.findings-acl.303
%P 5836-5849
Markdown (Informal)
[CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval](https://aclanthology.org/2025.findings-acl.303/) (Yu et al., Findings 2025)
ACL
- Heng Yu, Junfeng Kang, Rui Li, Qi Liu, Liyang He, Zhenya Huang, Shuanghong Shen, and Junyu Lu. 2025. CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval. In Findings of the Association for Computational Linguistics: ACL 2025, pages 5836–5849, Vienna, Austria. Association for Computational Linguistics.