QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs

TL;DR

QueueEDIT introduces a self-correction framework for sequential model editing in LLMs, improving factual accuracy and preserving general capabilities by dynamically managing knowledge-specific parameters during continuous updates.

Contribution

The paper proposes QueueEDIT, a novel queue-based self-correction method that enhances sequential model editing performance and mitigates negative impacts on LLMs' general abilities.

Findings

Significantly outperforms baselines in SME tasks

Maintains high NLP capabilities during editing

Effectively manages long-sequence dependencies

Abstract

Recently, large language models (LLMs) have demonstrated impressive results but still suffer from hallucinations. Model editing has been proposed to correct factual inaccuracies in LLMs. A challenging case is sequential model editing (SME), which aims to rectify errors continuously rather than treating them as a one-time task. During SME, the general capabilities of LLMs can be negatively affected due to the introduction of new parameters. In this paper, we propose a queue-based self-correction framework (QueueEDIT) that not only enhances SME performance by addressing long-sequence dependency but also mitigates the impact of parameter bias on the general capabilities of LLMs. Specifically, we first introduce a structural mapping editing loss to map the triplets to the knowledge-sensitive neurons within the Transformer layers of LLMs. We then store the located parameters for each piece of edited knowledge in a queue and dynamically align previously edited parameters. In each edit, we select queue parameters most relevant to the currently located parameters to determine whether previous knowledge needs realignment. Irrelevant parameters in the queue are frozen, and we update the parameters at the queue head to the LLM to ensure they do not harm general abilities. Experiments show that our framework significantly outperforms strong baselines across various SME settings and maintains competitiveness in single-turn editing. The resulting LLMs also preserve high capabilities in general NLP tasks throughout the SME process.