  Recent research has focused on using large language models (LLMs) to generate
explanations for hate speech through fine-tuning or prompting. Despite the
growing interest in this area, these generated explanations' effectiveness and
potential limitations remain poorly understood. A key concern is that these
explanations, generated by LLMs, may lead to erroneous judgments about the
nature of flagged content by both users and content moderators. For instance,
an LLM-generated explanation might inaccurately convince a content moderator
that a benign piece of content is hateful. In light of this, we propose an
analytical framework for examining hate speech explanations and conducted an
extensive survey on evaluating such explanations. Specifically, we prompted
GPT-3 to generate explanations for both hateful and non-hateful content, and a
survey was conducted with 2,400 unique respondents to evaluate the generated
explanations. Our findings reveal that (1) human evaluators rated the
GPT-generated explanations as high quality in terms of linguistic fluency,
informativeness, persuasiveness, and logical soundness, (2) the persuasive
nature of these explanations, however, varied depending on the prompting
strategy employed, and (3) this persuasiveness may result in incorrect
judgments about the hatefulness of the content. Our study underscores the need
for caution in applying LLM-generated explanations for content moderation. Code
and results are available at https://github.com/Social-AI-Studio/GPT3-HateEval.
