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.

通过语言模型提示和自我验证机制，NormSAGE框架可以发现符合人类交流和互动中的多语言、多文化规范，评估结果表明，与基线相比，该方法能够在语境中发现更多相关且有意义的规范，同时发现的中文对话的规范灵敏度和正确性与英文对话相当。该方法同时可以识别符合文化特定的规范，并通过可解释性和透明度支持动态实例化对话中的任何规范遵守和违规，并在传达自然语言解释时达到人类写作质量的水平。

NormSAGE: 基于实时对话的多语言多文化规范发现