Abstract Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized
natural language understanding and generation. They possess deep language …

Despite different countries and social platform regulations, digital abusive speech persists
as a significant challenge. One of the way to tackle abusive, or more specifically, toxic …

With the rapid development of artificial intelligence, large language models (LLMs) have
made remarkable progress in natural language processing. These models are trained on …

Addressing the pervasive issue of toxicity in online communication requires innovative
solutions beyond mere identification and removal of harmful content. This paper presents …

Text style transfer techniques are gaining popularity in natural language processing
allowing paraphrasing text in the required form: from toxic to neural, from formal to informal …

Multilingual text detoxification is a style transfer task of creating neutral versions of toxic texts
across multiple languages. In this paper, we use a mix of real and synthetic data to build a …

The task of text detoxification aims to re-write toxic text into non-toxic text. Though existing
methods have achieved impressive detoxification performance in monolingual settings …

The paper gives a brief overview of the four shared tasks organized at the PAN 2024 lab on
digital text forensics and stylometry to be hosted at CLEF 2024. The goal of the PAN lab is to …

Text detoxification is the task of transferring the style of text from toxic to neutral. While here
are approaches yielding promising results in monolingual setup, eg,(Dale et al., 2021; …

Data is a cornerstone for fine-tuning large language models, yet acquiring suitable data
remains challenging. Challenges encompassed data scarcity, linguistic diversity, and …