Language model detoxification aims to minimize the risk of generating
offensive or harmful content in pretrained language models (PLMs) for safer
deployment. Existing methods can be roughly categorized as finetuning-based and
decoding-based. However, the former is often resource-intensive, while the
latter relies on additional components and potentially compromises the
generation fluency. In this paper, we propose a more lightweight approach that
enables the PLM itself to achieve "self-detoxification". Our method is built
upon the observation that prepending a negative steering prompt can effectively
induce PLMs to generate toxic content. At the same time, we are inspired by the
recent research in the interpretability field, which formulates the evolving
contextualized representations within the PLM as an information stream
facilitated by the attention layers. Drawing on this idea, we devise a method
to identify the toxification direction from the normal generation process to
the one prompted with the negative prefix, and then steer the generation to the
reversed direction by manipulating the information movement within the
attention layers. Experimental results show that our approach, without any
fine-tuning or extra components, can achieve comparable performance with
state-of-the-art methods.

本文提出了一种轻量级方法，通过在预训练语言模型中添加负向指令来诱导模型生成无害内容，同时利用注意力层中的信息传递方式来逆转生成过程中的有害方向，实现语言模型自我脱毒。实验结果表明，我们的方法无需微调或额外组件，可达到与最先进方法相当的性能。