Evaluating GPT-3 Generated Explanations for Hateful Content Moderation

Understand

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.