{"ID":2828510,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.14395","arxiv_id":"2512.14395","title":"Massive Editing for Large Language Models Based on Dynamic Weight Generation","abstract":"Knowledge Editing (KE) is a field that studies how to modify some knowledge in Large Language Models (LLMs) at a low cost (compared to pre-training). Currently, performing large-scale edits on LLMs while ensuring the Reliability, Generality, and Locality metrics of the edits remain a challenge. This paper proposes a Massive editing approach for LLMs based on dynamic weight Generation (MeG). Our MeG involves attaching a dynamic weight neuron to specific layers of the LLMs and using a diffusion model to conditionally generate the weights of this neuron based on the input query required for the knowledge. This allows the use of adding a single dynamic weight neuron to achieve the goal of large-scale knowledge editing. Experiments show that our MeG can significantly improve the performance of large-scale KE in terms of Reliability, Generality, and Locality metrics compared to existing knowledge editing methods, particularly with a high percentage point increase in the absolute value index for the Locality metric, demonstrating the advantages of our proposed method. Code is available at https://github.com/RodeWayne/MeG-for-Knowledge-Editing.","short_abstract":"Knowledge Editing (KE) is a field that studies how to modify some knowledge in Large Language Models (LLMs) at a low cost (compared to pre-training). Currently, performing large-scale edits on LLMs while ensuring the Reliability, Generality, and Locality metrics of the edits remain a challenge. This paper proposes a Ma...","url_abs":"https://arxiv.org/abs/2512.14395","url_pdf":"https://arxiv.org/pdf/2512.14395v4","authors":"[\"Wentao Wan\",\"Qiqing Lao\",\"Zhiwei Xie\",\"Hefeng Wu\",\"Runnan Lin\",\"Liang Lin\",\"Keze Wang\"]","published":"2025-12-16T13:32:55Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Diffusion Model\",\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":605880,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2828510,"paper_url":"https://arxiv.org/abs/2512.14395","paper_title":"Massive Editing for Large Language Models Based on Dynamic Weight Generation","repo_url":"https://github.com/RodeWayne/MeG-for-Knowledge-Editing","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}