Computer Science > Computation and Language
[Submitted on 2 Feb 2023 (v1), last revised 22 Oct 2023 (this version, v3)]
Title:Using In-Context Learning to Improve Dialogue Safety
View PDFAbstract: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.
Submission history
From: Nicholas Meade [view email][v1] Thu, 2 Feb 2023 04:46:03 UTC (303 KB)
[v2] Tue, 23 May 2023 16:37:06 UTC (173 KB)
[v3] Sun, 22 Oct 2023 19:28:24 UTC (528 KB)
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