With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online. To combat nuanced forms of hate speech, it is important to identify and thoroughly explain hate speech to help users understand its harmful effects. Recent benchmarks have attempted to tackle this issue by training generative models on free-text annotations of implications in hateful text. However, we find significant reasoning gaps in the existing annotations schemes, which may hinder the supervision of detection models. In this paper, we introduce a hate speech detection framework, HARE, which harnesses the reasoning capabilities of large language models (LLMs) to fill these gaps in explanations of hate speech, thus enabling effective supervision of detection models. Experiments on SBIC and Implicit Hate benchmarks show that our method, using model-generated data, consistently outperforms baselines, using existing free-text human annotations. Analysis demonstrates that our method enhances the explanation quality of trained models and improves generalization to unseen datasets. Our code is available at https://github.com/joonkeekim/hare-hate-speech.git.

翻译：随着社交媒体的普及，仇恨言论的准确检测已成为确保网络环境安全的关键。为应对复杂形式的仇恨言论，识别并深度解释仇恨言论以帮助用户理解其危害至关重要。近期基准测试尝试通过训练生成模型对仇恨文本中的隐含意义进行自由文本标注以解决该问题。然而，我们发现现有标注方案存在显著推理缺陷，这可能阻碍检测模型的监督有效性。本文提出一个名为HARE的仇恨言论检测框架，该框架利用大语言模型的推理能力填补仇恨言论解释中的此类缺陷，从而实现对检测模型的有效监督。在SBIC与隐式仇恨基准上的实验表明，使用模型生成数据的本方法持续优于基于现有自由文本人工标注的基线方法。分析证明，本方法提升了训练模型的解释质量，并增强了对未见数据集的泛化能力。我们的代码已开源：https://github.com/joonkeekim/hare-hate-speech.git。