ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information
Zheng Hui, Zhaoxiao Guo, Hang Zhao, Juanyong Duan, Congrui Huang
Abstract
In different NLP tasks, detecting harmful content is crucial for online environments, especially with the growing influence of social media. However, previous research has two main issues: 1) a lack of data in low-resource settings, and 2) inconsistent definitions and criteria for judging harmful content, requiring classification models to be robust to spurious features and diverse. We propose Toxicraft, a novel framework for synthesizing datasets of harmful information to address these weaknesses. With only a small amount of seed data, our framework can generate a wide variety of synthetic, yet remarkably realistic, examples of toxic information. Experimentation across various datasets showcases a notable enhancement in detection model robustness and adaptability, surpassing or close to the gold labels.
BibTeX
@inproceedings{hui-etal-2024-toxicraft,
title = "{T}oxi{C}raft: A Novel Framework for Synthetic Generation of Harmful Information",
author = "Hui, Zheng and
Guo, Zhaoxiao and
Zhao, Hang and
Duan, Juanyong and
Huang, Congrui",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.970/",
doi = "10.18653/v1/2024.findings-emnlp.970",
pages = "16632--16647"
}