Learn What NOT to Learn: Towards Generative Safety in Chatbots

Understand

Conversational models that are generative and open-domain are particularly susceptible to generating unsafe content since they are trained on web-based social data.

• Prior approaches to mitigating this issue have drawbacks, such as disrupting the flow of conversation, limited generalization to unseen toxic input contexts, and sacrificing the quality of the dialogue for the sake of safety.
• In this paper, we present a novel framework, named "LOT" (Learn NOT to), that employs a contrastive loss to enhance generalization by learning from both positive and negative training signals.
• Our approach differs from the standard contrastive learning framework in that it automatically obtains positive and negative signals from the safe and unsafe language distributions that have been learned beforehand.