  Hate speech has become pervasive in today's digital age. Although there has
been considerable research to detect hate speech or generate counter speech to
combat hateful views, these approaches still cannot completely eliminate the
potential harmful societal consequences of hate speech -- hate speech, even
when detected, can often not be taken down or is often not taken down enough;
and hate speech unfortunately spreads quickly, often much faster than any
generated counter speech.
  This paper investigates a relatively new yet simple and effective approach of
suggesting a rephrasing of potential hate speech content even before the post
is made. We show that Large Language Models (LLMs) perform well on this task,
outperforming state-of-the-art baselines such as BART-Detox. We develop 4
different prompts based on task description, hate definition, few-shot
demonstrations and chain-of-thoughts for comprehensive experiments and conduct
experiments on open-source LLMs such as LLaMA-1, LLaMA-2 chat, Vicuna as well
as OpenAI's GPT-3.5. We propose various evaluation metrics to measure the
efficacy of the generated text and ensure the generated text has reduced hate
intensity without drastically changing the semantic meaning of the original
text.
  We find that LLMs with a few-shot demonstrations prompt work the best in
generating acceptable hate-rephrased text with semantic meaning similar to the
original text. Overall, we find that GPT-3.5 outperforms the baseline and
open-source models for all the different kinds of prompts. We also perform
human evaluations and interestingly, find that the rephrasings generated by
GPT-3.5 outperform even the human-generated ground-truth rephrasings in the
dataset. We also conduct detailed ablation studies to investigate why LLMs work
satisfactorily on this task and conduct a failure analysis to understand the
gaps.
