@inproceedings{han-etal-2025-temporal,
title = "Temporal Information Retrieval via Time-Specifier Model Merging",
author = "Han, SeungYoon and
Hwang, Taeho and
Cho, Sukmin and
Jeong, Soyeong and
Song, Hoyun and
Lee, Huije and
Park, Jong C.",
editor = "Zhang, Yuji and
Chen, Canyu and
Li, Sha and
Geva, Mor and
Han, Chi and
Wang, Xiaozhi and
Feng, Shangbin and
Gao, Silin and
Augenstein, Isabelle and
Bansal, Mohit and
Li, Manling and
Ji, Heng",
booktitle = "Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.knowllm-1.1/",
doi = "10.18653/v1/2025.knowllm-1.1",
pages = "1--13",
ISBN = "979-8-89176-283-1",
abstract = "The rapid expansion of digital information and knowledge across structured and unstructured sources has heightened the importance of Information Retrieval (IR). While dense retrieval methods have substantially improved semantic matching for general queries, they consistently underperform on queries with explicit temporal constraints{--}often those containing numerical expressions and time specifiers such as ``in 2015.'' Existing approaches to Temporal Information Retrieval (TIR) improve temporal reasoning but often suffer from catastrophic forgetting, leading to reduced performance on non-temporal queries. To address this, we propose Time-Specifier Model Merging (TSM), a novel method that enhances temporal retrieval while preserving accuracy on non-temporal queries. TSM trains specialized retrievers for individual time specifiers and merges them into a unified model, enabling precise handling of temporal constraints without compromising non-temporal retrieval. Extensive experiments on both temporal and non-temporal datasets demonstrate that TSM significantly improves performance on temporally constrained queries while maintaining strong results on non-temporal queries, consistently outperforming other training methods. Our code is available at https://github.com/seungyoonee/TSM."
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<abstract>The rapid expansion of digital information and knowledge across structured and unstructured sources has heightened the importance of Information Retrieval (IR). While dense retrieval methods have substantially improved semantic matching for general queries, they consistently underperform on queries with explicit temporal constraints–often those containing numerical expressions and time specifiers such as “in 2015.” Existing approaches to Temporal Information Retrieval (TIR) improve temporal reasoning but often suffer from catastrophic forgetting, leading to reduced performance on non-temporal queries. To address this, we propose Time-Specifier Model Merging (TSM), a novel method that enhances temporal retrieval while preserving accuracy on non-temporal queries. TSM trains specialized retrievers for individual time specifiers and merges them into a unified model, enabling precise handling of temporal constraints without compromising non-temporal retrieval. Extensive experiments on both temporal and non-temporal datasets demonstrate that TSM significantly improves performance on temporally constrained queries while maintaining strong results on non-temporal queries, consistently outperforming other training methods. Our code is available at https://github.com/seungyoonee/TSM.</abstract>
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%0 Conference Proceedings
%T Temporal Information Retrieval via Time-Specifier Model Merging
%A Han, SeungYoon
%A Hwang, Taeho
%A Cho, Sukmin
%A Jeong, Soyeong
%A Song, Hoyun
%A Lee, Huije
%A Park, Jong C.
%Y Zhang, Yuji
%Y Chen, Canyu
%Y Li, Sha
%Y Geva, Mor
%Y Han, Chi
%Y Wang, Xiaozhi
%Y Feng, Shangbin
%Y Gao, Silin
%Y Augenstein, Isabelle
%Y Bansal, Mohit
%Y Li, Manling
%Y Ji, Heng
%S Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-283-1
%F han-etal-2025-temporal
%X The rapid expansion of digital information and knowledge across structured and unstructured sources has heightened the importance of Information Retrieval (IR). While dense retrieval methods have substantially improved semantic matching for general queries, they consistently underperform on queries with explicit temporal constraints–often those containing numerical expressions and time specifiers such as “in 2015.” Existing approaches to Temporal Information Retrieval (TIR) improve temporal reasoning but often suffer from catastrophic forgetting, leading to reduced performance on non-temporal queries. To address this, we propose Time-Specifier Model Merging (TSM), a novel method that enhances temporal retrieval while preserving accuracy on non-temporal queries. TSM trains specialized retrievers for individual time specifiers and merges them into a unified model, enabling precise handling of temporal constraints without compromising non-temporal retrieval. Extensive experiments on both temporal and non-temporal datasets demonstrate that TSM significantly improves performance on temporally constrained queries while maintaining strong results on non-temporal queries, consistently outperforming other training methods. Our code is available at https://github.com/seungyoonee/TSM.
%R 10.18653/v1/2025.knowllm-1.1
%U https://aclanthology.org/2025.knowllm-1.1/
%U https://doi.org/10.18653/v1/2025.knowllm-1.1
%P 1-13
Markdown (Informal)
[Temporal Information Retrieval via Time-Specifier Model Merging](https://aclanthology.org/2025.knowllm-1.1/) (Han et al., KnowLLM 2025)
ACL
- SeungYoon Han, Taeho Hwang, Sukmin Cho, Soyeong Jeong, Hoyun Song, Huije Lee, and Jong C. Park. 2025. Temporal Information Retrieval via Time-Specifier Model Merging. In Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM), pages 1–13, Vienna, Austria. Association for Computational Linguistics.