  Text detoxification aims to minimize the risk of language models producing
toxic content. Existing detoxification methods of directly constraining the
model output or further training the model on the non-toxic corpus fail to
achieve a decent balance between detoxification effectiveness and generation
quality. This issue stems from the neglect of constrain imposed by the context
since language models are designed to generate output that closely matches the
context while detoxification methods endeavor to ensure the safety of the
output even if it semantically deviates from the context. In view of this, we
introduce a Context-aware Model self-Detoxification~(CMD) framework that pays
attention to both the context and the detoxification process, i.e., first
detoxifying the context and then making the language model generate along the
safe context. Specifically, CMD framework involves two phases: utilizing
language models to synthesize data and applying these data for training. We
also introduce a toxic contrastive loss that encourages the model generation
away from the negative toxic samples. Experiments on various LLMs have verified
the effectiveness of our MSD framework, which can yield the best performance
compared to baselines.
