  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.
