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.

翻译：在线社区中的规范违规检测对于维持健康安全的讨论空间至关重要。由于针对此类上下文特定任务微调模型的内在挑战，现有机器学习方法往往难以适应不同社区间多样化的规则与解释。本文提出面向规范违规检测的上下文感知提示学习（CPL-NoViD），这是一种采用提示学习来检测各类规则违规的新方法。通过将上下文融入自然语言提示，CPL-NoViD优于基线方法，并在不同规则类型上展现出更优性能。值得注意的是，该方法不仅在跨规则类型与跨社区的规范违规检测中表现出色，还展现出在少样本学习场景中的适应性。尤为重要的是，它建立了规范违规检测领域的最新最优性能，超越了现有基准。本研究凸显了提示学习在实现上下文敏感型规范违规检测方面的潜力，为开发更具适应性、上下文感知能力的模型以更好支持在线社区管理员的工作铺平了道路。