Identifying Knowledge Editing Types in Large Language Models
This work addresses the critical problem of preventing malicious misuse of knowledge editing in LLMs by providing a method to identify harmful edits, which is important for ensuring the safety and trustworthiness of LLMs for all users.
This paper introduces the Knowledge Editing Type Identification (KETI) task to identify different types of edits in Large Language Models (LLMs), including five harmful and one benign factual edit. The authors developed KETIBench and evaluated eight baseline classifiers across 92 trials, demonstrating decent identification performance and the feasibility of detecting malicious edits in LLMs.
Knowledge editing has emerged as an efficient technique for updating the knowledge of large language models (LLMs), attracting increasing attention in recent years. However, there is a lack of effective measures to prevent the malicious misuse of this technique, which could lead to harmful edits in LLMs. These malicious modifications could cause LLMs to generate toxic content, misleading users into inappropriate actions. In front of this risk, we introduce a new task, $\textbf{K}$nowledge $\textbf{E}$diting $\textbf{T}$ype $\textbf{I}$dentification (KETI), aimed at identifying different types of edits in LLMs, thereby providing timely alerts to users when encountering illicit edits. As part of this task, we propose KETIBench, which includes five types of harmful edits covering the most popular toxic types, as well as one benign factual edit. We develop five classical classification models and three BERT-based models as baseline identifiers for both open-source and closed-source LLMs. Our experimental results, across 92 trials involving four models and three knowledge editing methods, demonstrate that all eight baseline identifiers achieve decent identification performance, highlighting the feasibility of identifying malicious edits in LLMs. Additional analyses reveal that the performance of the identifiers is independent of the reliability of the knowledge editing methods and exhibits cross-domain generalization, enabling the identification of edits from unknown sources. All data and code are available in https://github.com/xpq-tech/KETI.