Forget for Get: A Lightweight Two-phase Gradient Method for Knowledge Editing in Large Language Models

ACL ARR 2025 February Submission2173 Authors

Abstract:

Recent studies have highlighted the remarkable knowledge retention capabilities of Large Language Models (LLMs) like GPT-4, while simultaneously revealing critical limitations in maintaining knowledge currency and accuracy.
Existing knowledge editing methodologies, designed to update specific factual information without compromising general model performance, often encounter two fundamental challenges: parameter conflict during knowledge overwriting and excessive computational overhead.
In this paper, we introduce ForGet (Forget for Get), a novel approach grounded in the principle of "forgetting before learning". By pinpointing the location within the LLM that corresponds to the target knowledge, we first erase the outdated knowledge and then insert the new knowledge at this precise spot. ForGet is the first work to leverage a two-phase gradient-based process for knowledge editing, offering a lightweight solution that also delivers superior results. Experimental findings show that our method achieves more effective knowledge editing at a lower cost compared to previous techniques across various base models.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: Generalization, generative models

Contribution Types: NLP engineering experiment, Theory

Languages Studied: English

Submission Number: 2173