Evaluating GPT-3 Generated Explanations for Hateful Content Moderation

Abstract

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.

Hate speech detection in social media: Techniques, recent trends, and future challenges

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Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom

“…Also, some works reveal that ChatGPT evaluation produces results similar to expert human evaluation [8,13]. Therefore, we engage ChatGPT to evaluate the quality of explanations based on four metrics which have been widely employed in human evaluation [36,40]: misleadingness, informativeness, soundness, and readability. A 5-point Likert scale was employed, where 1 represented the poorest and 5 the best in addition to misleadingness.…”