Work

TamEdit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing

Year: 2026

Type: conference-paper

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; Trajectorybased 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 sub-second inference speed (0.55s per edit), while preserving general capabilities.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.979

Authors: Shiqiang Tian, Cheng Ding, Qin Chen, Jie Zhou, Liang He

Institution: East China Normal University

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Shanghai Municipal Commission of Economy and Informatization

Awards: G3000942568, G4966914577, G6250510606, G7864132582