  Norm discovery is important for understanding and reasoning about the
acceptable behaviors and potential violations in human communication and
interactions. We introduce NormSage, a framework for addressing the novel task
of conversation-grounded multi-lingual, multi-cultural norm discovery, based on
language model prompting and self-verification. NormSAGE leverages the
expressiveness and implicit knowledge of the pretrained GPT-3 language model
backbone, to elicit knowledge about norms through directed questions
representing the norm discovery task and conversation context. It further
addresses the risk of language model hallucination with a self-verification
mechanism ensuring that the norms discovered are correct and are substantially
grounded to their source conversations. Evaluation results show that our
approach discovers significantly more relevant and insightful norms for
conversations on-the-fly compared to baselines (>10+% in Likert scale rating).
The norms discovered from Chinese conversation are also comparable to the norms
discovered from English conversation in terms of insightfulness and correctness
(<3% difference). In addition, the culture-specific norms are promising
quality, allowing for 80% accuracy in culture pair human identification.
Finally, our grounding process in norm discovery self-verification can be
extended for instantiating the adherence and violation of any norm for a given
conversation on-the-fly, with explainability and transparency. NormSAGE
achieves an AUC of 95.4% in grounding, with natural language explanation
matching human-written quality.
