DeltaEdit: Enhancing Sequential Editing in Large Language Models by Controlling Superimposed Noise

Abstract: Sequential knowledge editing techniques aim to continuously update the knowledge in LLMs at a low cost, preventing the models from generating outdated or incorrect information. However, existing sequential editing methods suffer from a significant decline in editing success rates after long-term editing. Through theoretical analysis and experiments, we identify that as the number of edits increases, the model's output increasingly deviates from the desired target, leading to a drop in editing success rates. We refer to this issue as the accumulation of superimposed noise problem. To address this, we identify the factors contributing to this deviation and propose DeltaEdit, a novel method that optimizes update parameters through a dynamic orthogonal constraints strategy, effectively reducing interference between edits to mitigate deviation. Experimental results demonstrate that DeltaEdit significantly outperforms existing methods in edit success rates and the retention of generalization capabilities, ensuring stable and reliable model performance even under extensive sequential editing.