  Harmful and offensive communication or content is detrimental to social
bonding and the mental state of users on social media platforms. Text
detoxification is a crucial task in natural language processing (NLP), where
the goal is removing profanity and toxicity from text while preserving its
content. Supervised and unsupervised learning are common approaches for
designing text detoxification solutions. However, these methods necessitate
fine-tuning, leading to computational overhead. In this paper, we propose
GPT-DETOX as a framework for prompt-based in-context learning for text
detoxification using GPT-3.5 Turbo. We utilize zero-shot and few-shot prompting
techniques for detoxifying input sentences. To generate few-shot prompts, we
propose two methods: word-matching example selection (WMES) and
context-matching example selection (CMES). We additionally take into account
ensemble in-context learning (EICL) where the ensemble is shaped by base
prompts from zero-shot and all few-shot settings. We use ParaDetox and APPDIA
as benchmark detoxification datasets. Our experimental results show that the
zero-shot solution achieves promising performance, while our best few-shot
setting outperforms the state-of-the-art models on ParaDetox and shows
comparable results on APPDIA. Our EICL solutions obtain the greatest
performance, adding at least 10% improvement, against both datasets.
