Towards Lifelong Model Editing via Simulating Ideal Editor

Yaming Guo, Siyang Guo, Hengshu Zhu, Ying Sun

TL;DR: We propose a general framework that restores the strong performance of standard model editing methods in a lifelong context, bridging the gap between these two paradigms for the first time.

Abstract: Model editing plays a crucial role in the cost-effective development of large language models, and the challenge of evolving knowledge facilitates its sequential extension, namely lifelong model editing. However, progress on standard and lifelong editing has historically followed separate tracks, overlooking the potential of generalizing standard methods to lifelong scenarios. By establishing this bridge, we can provide robust baselines in lifelong scenarios and ensure that lifelong editing benefits from the ongoing advancements in standard editing technologies. In response, this paper proposes a general framework, ***Sim**ulating **I**deal **E**ditor* (SimIE), which restores the strong performance of parameter-modifying methods from standard model editing in a lifelong context. SimIE formulates the ideal parameter shift as the minimum-norm solution to a linear system, constructed using the Moore-Penrose inverse, and subsequently enables recursive updates by truncating the limiting expression of the Moore-Penrose inverse under two mild assumptions. Theoretically, we demonstrate that if either assumption is not met, the solution provided by SimIE remains near-optimal in a statistical sense or stable against perturbations introduced by the sequential editing, but a trade-off between optimality and stability arises when both assumptions fail. Extensive experiments validate the effectiveness of SimIE, which allows standard algorithms to achieve performance comparable to specialized lifelong model editing methods. Our code is available at https://github.com/YamingGuo98/SimIE.

Lay Summary:

Large language models, such as ChatGPT, are powerful tools, but updating them with new information or correcting errors can be difficult and expensive. Historically, researchers have treated single-time updates (standard editing) and continuous updates (lifelong editing) as separate challenges, limiting the ability to utilize proven standard methods in lifelong scenarios.

To bridge this gap, we introduce the Simulating Ideal Editor (SimIE), a general framework that restores the strong performance of standard model editing in a lifelong context. SimIE determines the ideal updates to a model’s parameters, ensuring effective and continuous knowledge integration. Even in imperfect conditions, SimIE is still valid, though a trade-off is needed between maximizing optimality and preserving stability.

Our experiments demonstrate that SimIE enables standard editing methods to perform as effectively as specialized lifelong approaches, thus benefiting lifelong editing from ongoing advancements made in standard techniques.

Link To Code: https://github.com/YamingGuo98/SimIE

Primary Area: Deep Learning->Large Language Models

Keywords: Lifelong Model Editing, Linear System, Large Language Model

Submission Number: 2690