Knowledge Editing for Large Language Model with Knowledge Neuronal Ensemble

• URL: http://arxiv.org/abs/2412.20637v1
• Date: Mon, 30 Dec 2024 00:58:00 GMT
• Title: Knowledge Editing for Large Language Model with Knowledge Neuronal Ensemble
• Authors: Yongchang Li, Yujin Zhu, Tao Yan, Shijian Fan, Gang Wu, Liang Xu,
• Abstract summary: We propose a novel knowledge editing method called Knowledge Neuronal Ensemble (KNE)<n>A knowledge neuronal ensemble represents a group of neurons encoding specific knowledge, thus mitigating the issue of frequent parameter modification.<n> Experimental results on three widely used knowledge editing datasets show that the KNE method significantly improves the accuracy of knowledge editing.
• Score: 13.608354678065222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As real-world knowledge is constantly evolving, ensuring the timeliness and accuracy of a model's knowledge is crucial. This has made knowledge editing in large language models increasingly important. However, existing knowledge editing methods face several challenges, including parameter localization coupling, imprecise localization, and a lack of dynamic interaction across layers. In this paper, we propose a novel knowledge editing method called Knowledge Neuronal Ensemble (KNE). A knowledge neuronal ensemble represents a group of neurons encoding specific knowledge, thus mitigating the issue of frequent parameter modification caused by coupling in parameter localization. The KNE method enhances the precision and accuracy of parameter localization by computing gradient attribution scores for each parameter at each layer. During the editing process, only the gradients and losses associated with the knowledge neuronal ensemble are computed, with error backpropagation performed accordingly, ensuring dynamic interaction and collaborative updates among parameters. Experimental results on three widely used knowledge editing datasets show that the KNE method significantly improves the accuracy of knowledge editing and achieves, or even exceeds, the performance of the best baseline methods in portability and locality metrics.

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