面向公共论坛的具有法律可执行性的仇恨言论检测
Towards Legally Enforceable Hate Speech Detection for Public Forums
AI总结：
本文提出以法律定义为核心的可执行仇恨言论检测任务，构建由法律专家标注的数据集并扩充专家样本与挑战集，通过零样本和少样本提示使大语言模型决策对齐法律定义，提升公共论坛仇恨言论执法的严谨性。
摘要：
仇恨言论引发广泛而深刻的社会问题。妥善执行仇恨言论法律是保护群体免受有害和歧视性语言侵害的关键。然而，判定何种内容构成仇恨言论是一项复杂的任务，高度依赖主观解释。现有研究未将其系统与可执行的仇恨言论定义对齐，这可能导致其输出与监管机构的目标不一致。本研究引入了一种以法律定义为核心的可执行仇恨言论检测的新视角和新任务，并构建了一个由法律专家根据十一种可能定义标注违规情况的数据集。鉴于识别清晰、法律上可执行的仇恨言论实例存在挑战，我们通过专家生成的样本和自动挖掘的挑战集对数据集进行了扩充。我们尝试使用零样本和少样本提示，将模型决策建立在这些定义之上，并在多个大语言模型（LLMs）上报告了实验结果。通过这一任务定义，自动仇恨言论检测能够更紧密地与可执行法律对齐，从而有助于在公共论坛中对有害言论的法律保护进行更严格的执行。
英文摘要：
Hate speech causes widespread and deep-seated societal issues. Proper enforcement of hate speech laws is key for protecting groups of people against harmful and discriminatory language. However, determining what constitutes hate speech is a complex task that is highly open to subjective interpretations. Existing works do not align their systems with enforceable definitions of hate speech, which can make their outputs inconsistent with the goals of regulators. This research introduces a new perspective and task for enforceable hate speech detection centred around legal definitions, and a dataset annotated on violations of eleven possible definitions by legal experts. Given the challenge of identifying clear, legally enforceable instances of hate speech, we augment the dataset with expert-generated samples and an automatically mined challenge set. We experiment with grounding the model decision in these definitions using zero-shot and few-shot prompting. We then report results on several large language models (LLMs). With this task definition, automatic hate speech detection can be more closely aligned to enforceable laws, and hence assist in more rigorous enforcement of legal protections against harmful speech in public forums.
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。
