PclGPT: A Large Language Model for Patronizing and Condescending Language Detection
<|reference_start|>PclGPT: A Large Language Model for Patronizing and Condescending Language Detection: Disclaimer: Samples in this paper may be harmful and cause discomfort!   Patronizing and condescending language (PCL) is a form of speech directed at vulnerable groups. As an essential branch of toxic language, this type of language exacerbates conflicts and confrontations among Internet communities and detrimentally impacts disadvantaged groups. Traditional pre-trained language models (PLMs) perform poorly in detecting PCL due to its implicit toxicity traits like hypocrisy and false sympathy. With the rise of large language models (LLMs), we can harness their rich emotional semantics to establish a paradigm for exploring implicit toxicity. In this paper, we introduce PclGPT, a comprehensive LLM benchmark designed specifically for PCL. We collect, annotate, and integrate the Pcl-PT/SFT dataset, and then develop a bilingual PclGPT-EN/CN model group through a comprehensive pre-training and supervised fine-tuning staircase process to facilitate implicit toxic detection. Group detection results and fine-grained detection from PclGPT and other models reveal significant variations in the degree of bias in PCL towards different vulnerable groups, necessitating increased societal attention to protect them.<|reference_end|>
@article{wang2024pclgpt:,
    title={PclGPT: A Large Language Model for Patronizing and Condescending
  Language Detection},
    author={Hongbo Wang and Mingda Li and Junyu Lu and Hebin Xia and Liang Yang
  and Bo Xu and Ruizhu Liu and Hongfei Lin},
    journal={arXiv preprint arXiv:2410.00361},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00361},
    primaryClass={cs.CL}
}