Detecting norm violations in online communities is critical to maintaining
healthy and safe spaces for online discussions. Existing machine learning
approaches often struggle to adapt to the diverse rules and interpretations
across different communities due to the inherent challenges of fine-tuning
models for such context-specific tasks. In this paper, we introduce
Context-aware Prompt-based Learning for Norm Violation Detection (CPL-NoViD), a
novel method that employs prompt-based learning to detect norm violations
across various types of rules. CPL-NoViD outperforms the baseline by
incorporating context through natural language prompts and demonstrates
improved performance across different rule types. Significantly, it not only
excels in cross-rule-type and cross-community norm violation detection but also
exhibits adaptability in few-shot learning scenarios. Most notably, it
establishes a new state-of-the-art in norm violation detection, surpassing
existing benchmarks. Our work highlights the potential of prompt-based learning
for context-sensitive norm violation detection and paves the way for future
research on more adaptable, context-aware models to better support online
community moderators.

本研究提出了一种使用基于提示的学习，用于检测在线社区中的规范违规行为。CPL-NoViD 通过自然语言提示将上下文融合到模型中，展示了对于不同类型的规则具有改进的性能，它不仅在跨规则类型和跨社区规范违反检测方面表现出色，而且在少样本学习场景中也表现出了适应性，取得了超越现有基准的新的最佳表现。