"Hiding in Plain Sight": Designing Synthetic Dialog Generation for Uncovering Socially Situated Norms

TL;DR

This paper introduces a novel framework for generating synthetic dialogues that automatically uncover social norms from rich interactions, facilitating better understanding and detection of norm violations without relying on predefined labels.

Contribution

The authors propose a multi-step framework for self-assessment-based norm discovery and create NormHint, a detailed synthetic dialogue dataset with norm violation annotations and remediation suggestions.

Findings

NormHint captures diverse, realistic conversations with high naturalness.

Fine-tuning models on norm violation data improves detection of social norm breaches.

The framework enables automatic uncovering of social norms from complex interactions.

Abstract

Naturally situated conversations encapsulate the social norms inherent to their context, reflecting both the relationships between interlocutors and the underlying communicative intent. In this paper, we propose a novel, multi-step framework for generating dialogues that automatically uncovers social norms from rich, context-laden interactions through a process of self-assessment and norm discovery, rather than relying on predefined norm labels. Leveraging this framework, we construct NormHint, a comprehensive synthetic dialogue dataset spanning a wide range of interlocutor attributes (e.g., age, profession, personality), relationship types, conversation topics, and conversational trajectories. NormHint is meticulously annotated with turn-level norm violation information, detailed participant descriptions, and remediation suggestions-including alternative trajectories achieved through early intervention. Human validation and automated analysis demonstrate that our dataset captures diverse conversational topics with high naturalness and realism. Moreover, we discovered that fine-tuning a model with our norm violation data significantly enhances its ability to detect and understand potential norm violations in conversations.