Chain of Explanation: New Prompting Method to Generate Higher Quality Natural Language Explanation for Implicit Hate Speech

The potential of sequence-to-sequence (Seq2Seq) models and prompting Recent studies have exploited advanced generative language models methods has not been fully explored [4]. Moreover, traditional evaluation to generate Natural Language Explanations (NLE) for why a certain metrics, such as BLEU [20] and Rouge [18], applied in NLE text could be hateful. We propose the Chain of Explanation (CoE) generation for hate speech, may also not be able to comprehensively Prompting method, using the heuristic words and target group, to capture the quality of the generated explanations because they generate high-quality NLE for implicit hate speech. We improved heavily rely on the word-level overlaps [3]. To fill those gaps, we the BLUE score from 44.0 to 62.3 for NLE generation by providing propose a Chain of Explanations (CoE) prompt method to generate accurate target information. We then evaluate the quality of generated high-quality NLE distinguishing the implicit hate speech from nonhateful NLE using various automatic metrics and human annotations tweets.