Learning to edit: Aligning llms with knowledge editing
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Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.
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The latest lifelong editing methods such as LTE, MALMEN, and RECIPE require extensive training with domain-specific edits before specific editing.
“The latest efforts in lifelong editing such as LTE [66], MALMEN [15], and RECIPE [67] require extensive training with domain-specific edits before specific editing”
From WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models (2024) · cites this paper as [66]
The Abstract and Limitations describe a one-time fine-tuning phase before LTE can perform real-time edits. Section 3.1 reports training on 60,000 examples constructed from knowledge-editing datasets, and §5.2 says alignment took about nine hours in the experiments. The paper does not establish that LTE requires edits specific to the domain of each subsequent editing task. This verdict concerns LTE, the method cited to this source, not MALMEN or RECIPE. (LLM, conf=0.85)
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- Authors
- Jiang, Y., Wang, Y., Wu, C., Zhong, W., Zeng, X., Gao, J., Li, L., Jiang, X., Shang, L., Tang, R., Liu, Q., & Wang, W.
- Affiliations
- The Hong Kong University of Science and Technology (Guangzhou) (CN)
- Hong Kong University of Science and Technology (HK)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 4689-4705
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402671939
- Cited by
- 12
- References
- 0
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 2
- FWCI
- 3.54
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- Hong Kong University of Science and Technology (2023A03J0013)
- Guangzhou Municipal Science and Technology Project (2023A03J0013, 2023A03J0003)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- knowledge editing, large language models, knowledge alignment, knowledge base editing
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