CMD: a framework for Context-aware Model self-Detoxification
This addresses the issue of toxic content generation in language models for users requiring safe AI interactions, representing an incremental improvement over existing detoxification methods.
The paper tackles the problem of balancing detoxification effectiveness and generation quality in language models by introducing a Context-aware Model self-Detoxification (CMD) framework that first detoxifies the context and then generates safe outputs, achieving the best performance compared to baselines in experiments.
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.