用合成对话自动发现社交规范，提升模型对人际互动规则的理解能力。
"Hiding in Plain Sight": Designing Synthetic Dialog Generation for Uncovering Socially Situated Norms
- 通过自评估与规范发现机制生成对话，不依赖预设标签。
- 构建了包含多种人物属性与对话轨迹的高质量合成数据集。
- 适合研究社交规范、对话理解与伦理对齐的学者与工程师。
自然语境中的对话蕴含着深层的社会规范，体现对话者间的关系与沟通意图。本文提出一种多阶段框架，通过自我评估与规范发现机制，从丰富上下文的交互中自动挖掘社会规范，而非依赖预定义标签。基于该框架，我们构建了 NormHint——一个涵盖广泛对话者属性（如年龄、职业、性格）、关系类型、话题及对话发展路径的合成对话数据集。该数据集在每一轮对话中精细标注了规范违反情况、参与者详细描述及修复建议，包括早期干预带来的替代路径。人工验证与自动化分析表明，该数据集在多样话题上具备高度自然性与真实性。此外，实验发现，使用我们的规范违反数据微调模型后，其识别和理解对话中潜在规范违反的能力显著提升。
原文摘要 · Abstract (English)
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.
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