@inproceedings{tian-etal-2026-tamedit,
    title = "{T}am{E}dit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing",
    author = "Tian, Shiqiang  and
      Ding, Cheng  and
      Chen, Qin  and
      Zhou, Jie  and
      He, Liang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.979/",
    pages = "21417--21439",
    ISBN = "979-8-89176-390-6",
    abstract = "Knowledge editing is a promising method for updating Large Language Models efficiently. However, previous studies often suffer from poor specificity in continual editing, as they typically focus on single edits or preventing knowledge forgetting. To address this, we propose TamEdit, a trajectory-aware meta-learning method that preserves specificity for continual knowledge editing. TamEdit unifies three levels: Inner Optimization performs multi-step fast fine-tuning on the single edit; Trajectory-based Editing unifies continual edits with a growing memory; and Outer Optimization leverages meta-learning to distill cross-task strategies for preserving specificity. By capturing the relationships between different single edits within the trajectory, our method learns how to effectively avoid specificity drift. Experiments across multiple LLMs show TamEdit significantly outperforms baselines in continual editing, improving specificity by 14.81{\%} with fast speed (requiring only 8.84{\%} of the time cost of most baselines), while preserving general capabilities."
}