  This paper investigates using knowledge editing techniques to detoxify Large
Language Models (LLMs). We construct a benchmark, SafeEdit, which covers nine
unsafe categories with various powerful attack prompts and equips comprehensive
metrics for systematic evaluation. We conduct experiments with several
knowledge editing approaches, indicating that knowledge editing has the
potential to detoxify LLMs with a limited impact on general performance
efficiently. Then, we propose a simple yet effective baseline, dubbed
Detoxifying with Intraoperative Neural Monitoring (DINM), to diminish the
toxicity of LLMs within a few tuning steps via only one instance. We further
provide an in-depth analysis of the internal mechanism for various detoxifying
approaches, demonstrating that previous methods like SFT and DPO may merely
suppress the activations of toxic parameters, while DINM mitigates the toxicity
of the toxic parameters to a certain extent, making permanent adjustments. We
hope that these insights could shed light on future work of developing
detoxifying approaches and the underlying knowledge mechanisms of LLMs. Code
and benchmark are available at https://github.com/zjunlp/EasyEdit.
