Can We Continually Edit Language Models? On the Knowledge Attenuation in Sequential Model Editing (2024)

Abstract: Model editing has become a promising method for precisely and effectively updating knowledge in language models. In this paper, we investigate knowledge attenuation, in which the retention of updated knowledge within the language model decreases as the number of edits increases after sequential editing. Through empirical study, we discovered that existing editing methods generally suffer from knowledge attenuation. We attribute this phenomenon to two aspects: (1) redundant parameters interference and (2) update weight disentanglement. To this end, we propose the AdaPLE method. It not only mitigates the knowledge attenuation issue but also improves the performance on existing benchmarks. To the best of our knowledge, we are the first to investigate the cause and mitigation of knowledge attenuation in sequential LLM editing.

## introduction recently, large language models (llm) like llama @xcite , falcon @xcite , and bloom @xcite have shown impressive capabilities in generating humanlike text, improving performance on a variety of natural language processing tasks. though llm is capable of performing tasks based on accumulated knowledge @xcite , they are prone to generating erroneous, harmful, or outdated information. thus the ability to maintain fresh and customized information is desirable in many scenarios. due to hardware and resource constraints, researchers seek methods to update knowledge inside llm efficiently. recent years have witnessed a growing number of precise yet efficient model editing techniques like mend @xcite , memit @xcite , and pmet @xcite , as shown in figure 1 , thus circumventing prohibitive fine-tuning process. ## preliminary in this section, we provide comprehensive preliminaries of sequential model editing. ## a comprehensive study of ka in this section, we present a comprehensive study of knowledge attenuation after sequentially editing the language model. we first give a geometric intuition for sequential editing, then we perform a series of investigations to explore the primary factors of knowledge attenuation. ## proposed method: adaple in this section, we introduce adaple , an effective editing method for mitigating the ka in language model editing. it can effectively mitigate knowledge attenuation by introducing adaptive merging of update weight. our proposed adaple follows the locate-then-edit style and composes two phases: (i) computing edit weight and (ii) adaptive merging of edit weight. in the computing stage, adaple first computes update weight within an optimization process (in section 4.1). once computed, the update weights are merged to edit targets during the adaptive merging process (in section 4.2). the pseudo-code is in algorithm 1. for more details, please refer to appendix c. ## experiment in this section, we study several axes of the existing knowledge editing methods, including a) overall performance, and b) performance of undergoing multiple simultaneous edits. more experimental results are in appendix d. ## discussion in this section, we provide more discussion for further understanding of ka and proposed method. ## conclusion in this paper, we systemically investigate knowledge attenuation of sequential edited language models. we analyze the potential causes of ka and attribute them to the primary defects of current editing methods, including (1) redundant parameters interference and (2) update weight disentanglement. to this end, we propose adaple . empirical studies indicate that it can effectively mitigate ka of sequentially edited language models while performing better on editing efficiency compared to existing model editing methods. it must be noted that our method can only mitigate ka to a certain extent, and we look forward to more work that can address this issue.