xList-Hate: 一个基于检查表的框架用于可解释且可推广的仇恨言论检测
xList-Hate: A Checklist-Based Framework for Interpretable and Generalizable Hate Speech Detection
AI总结：
xList-Hate通过基于检查表的诊断框架，提升仇恨言论检测的可解释性和跨领域鲁棒性。
摘要：
仇恨言论检测通常被框架为直接的二元分类问题，尽管它是一个通过多个相互作用因素定义的复合概念，这些因素在法律框架、平台政策和注释指南中有所不同。因此，监督模型往往过度拟合数据集特定的定义，并在领域转移和注释噪声下表现出有限的鲁棒性。我们引入了xList-Hate，一个诊断框架，将仇恨言论检测分解为一个基于广泛共享规范性标准的显式、概念级问题的清单。每个问题由大型语言模型（LLM）独立回答，生成二进制诊断表示，捕捉仇恨内容的特征而不直接预测最终标签。这些诊断信号随后通过轻量级、完全可解释的决策树进行聚合，产生透明且可审计的预测。我们评估了其在多个仇恨言论基准和模型家族上的表现，与零样本LLM分类和领域内监督微调进行比较。尽管监督方法通常最大化领域内性能，我们一致地提高了跨数据集鲁棒性和领域转移下的相对性能。此外，对分歧案例的定性分析提供了证据，表明该框架对某些形式的注释不一致和上下文模糊性更不敏感。关键的是，这种方法通过显式的决策路径和因素级分析实现了细粒度的可解释性。我们的结果表明，将仇恨言论检测重新框架为诊断推理任务，而不是单一分类问题，为内容审核提供了一种稳健、可解释且可扩展的替代方案。
英文摘要：
Hate speech detection is commonly framed as a direct binary classification problem despite being a composite concept defined through multiple interacting factors that vary across legal frameworks, platform policies, and annotation guidelines. As a result, supervised models often overfit dataset-specific definitions and exhibit limited robustness under domain shift and annotation noise. We introduce xList-Hate, a diagnostic framework that decomposes hate speech detection into a checklist of explicit, concept-level questions grounded in widely shared normative criteria. Each question is independently answered by a large language model (LLM), producing a binary diagnostic representation that captures hateful content features without directly predicting the final label. These diagnostic signals are then aggregated by a lightweight, fully interpretable decision tree, yielding transparent and auditable predictions. We evaluate it across multiple hate speech benchmarks and model families, comparing it against zero-shot LLM classification and in-domain supervised fine-tuning. While supervised methods typically maximize in-domain performance, we consistently improves cross-dataset robustness and relative performance under domain shift. In addition, qualitative analysis of disagreement cases provides evidence that the framework can be less sensitive to certain forms of annotation inconsistency and contextual ambiguity. Crucially, the approach enables fine-grained interpretability through explicit decision paths and factor-level analysis. Our results suggest that reframing hate speech detection as a diagnostic reasoning task, rather than a monolithic classification problem, provides a robust, explainable, and extensible alternative for content moderation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
