  While large neural-based conversational models have become increasingly
proficient dialogue agents, recent work has highlighted safety issues with
these systems. For example, these systems can be goaded into generating toxic
content, which often perpetuates social biases or stereotypes. We investigate a
retrieval-based method for reducing bias and toxicity in responses from
chatbots. It uses in-context learning to steer a model towards safer
generations. Concretely, to generate a response to an unsafe dialogue context,
we retrieve demonstrations of safe responses to similar dialogue contexts. We
find our method performs competitively with strong baselines without requiring
training. For instance, using automatic evaluation, we find our best fine-tuned
baseline only generates safe responses to unsafe dialogue contexts from
DiaSafety 4.04% more than our approach. Finally, we also propose a re-ranking
procedure which can further improve response safeness.
