One for All: Update Parameterized Knowledge Across Multiple Models Article Swipe
Weitao Ma
,
Xiyuan Du
,
Xiaocheng Feng
,
Lei Huang
,
Yudong Huang
,
Huiyi Zhang
,
Xiaoliang Yang
,
Baohang Li
,
Xiachong Feng
,
Ting Liu
,
Bing Qin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2506.00817
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2506.00817
Large language models (LLMs) encode vast world knowledge but struggle to stay up-to-date, often leading to errors and hallucinations. Knowledge editing offers an efficient alternative to retraining, enabling targeted modifications by updating specific model parameters. However, existing methods primarily focus on individual models, posing challenges in efficiently updating multiple models and adapting to new models. To address this, we propose OnceEdit, a novel ensemble-based approach that employs a plug-in model as the editing module, enabling stable knowledge updates across multiple models. Building on the model ensemble, OnceEdit introduces two key mechanisms to enhance its effectiveness. First, we introduce a dynamic weight mechanism through a \weight token for distinguishing between edit-related and non-edit-related instances, ensuring the appropriate utilization of knowledge from integrated models. Second, we incorporate an ensemble enhancement mechanism to mitigate the excessive reliance on the central model inherent in the model ensemble technique, making it more suitable for knowledge editing. Extensive experiments on diverse LLMs demonstrate that OnceEdit consistently outperforms existing methods while achieving superior editing efficiency. Further analysis confirms its adaptability and stability in multi-model editing scenarios. Our code will be available.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2506.00817
- https://arxiv.org/pdf/2506.00817
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4414893848
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4414893848Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2506.00817Digital Object Identifier
- Title
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One for All: Update Parameterized Knowledge Across Multiple ModelsWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-06-01Full publication date if available
- Authors
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Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yudong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing QinList of authors in order
- Landing page
-
https://arxiv.org/abs/2506.00817Publisher landing page
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https://arxiv.org/pdf/2506.00817Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2506.00817Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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