Language Model Detoxification in Dialogue with Contextualized Stance Control
This work addresses the issue of toxic degeneration in language models for dialogue systems, representing an incremental improvement over previous detoxification methods.
The paper tackles the problem of language model detoxification by addressing implicit offensive language that supports offensive context, proposing a context-dependent stance control method. The result shows effective learning of context-dependent strategies while maintaining low self-toxicity.
To reduce the toxic degeneration in a pretrained Language Model (LM), previous work on Language Model detoxification has focused on reducing the toxicity of the generation itself (self-toxicity) without consideration of the context. As a result, a type of implicit offensive language where the generations support the offensive language in the context is ignored. Different from the LM controlling tasks in previous work, where the desired attributes are fixed for generation, the desired stance of the generation depends on the offensiveness of the context. Therefore, we propose a novel control method to do context-dependent detoxification with the stance taken into consideration. We introduce meta prefixes to learn the contextualized stance control strategy and to generate the stance control prefix according to the input context. The generated stance prefix is then combined with the toxicity control prefix to guide the response generation. Experimental results show that our proposed method can effectively learn the context-dependent stance control strategies while keeping a low self-toxicity of the underlying LM.