KoLEG: On-the-Fly Korean Legal Knowledge Editing with Continuous Retrieval

Jaehyung Seo, Dahyun Jung, Jaewook Lee, Yongchan Chun, Dongjun Kim, Hwijung Ryu, Donghoon Shin, Heuiseok Lim


Abstract
Korean legal knowledge is subject to frequent temporal updates driven by societal needs and government policies. Even minor modifications to legal provisions can have significant consequences, yet continuously retraining large language models (LLMs) to incorporate such updates is resource-intensive and impractical. To address this, we propose KoLEG, an on-the-fly Korean Legal knowledge editing framework enhanced with continuous retrieval. KoLEG employs an Editing-Aware Learning Strategy and a LawEdit Retriever, which together adaptively integrate subtle linguistic nuances and continuous legislative amendments. To support this task, we construct the Korean Legislative Amendment Dataset, explicitly designed for continuous legal knowledge updates with attention to both temporal dynamics and linguistic subtleties. KoLEG outperforms existing locate-then-edit and retrieval-based editing methods, demonstrating superior effectiveness in legal knowledge editing while preserving linguistic capabilities. Furthermore, KoLEG maintains robust performance in sequential editing, improves performance on precedent application tasks, and is qualitatively validated by legal experts.
Anthology ID:
2025.findings-emnlp.489
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2025
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9191–9217
Language:
URL:
https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.489/
DOI:
10.18653/v1/2025.findings-emnlp.489
Bibkey:
Cite (ACL):
Jaehyung Seo, Dahyun Jung, Jaewook Lee, Yongchan Chun, Dongjun Kim, Hwijung Ryu, Donghoon Shin, and Heuiseok Lim. 2025. KoLEG: On-the-Fly Korean Legal Knowledge Editing with Continuous Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 9191–9217, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
KoLEG: On-the-Fly Korean Legal Knowledge Editing with Continuous Retrieval (Seo et al., Findings 2025)
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https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.489.pdf
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