ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information
<|reference_start|>ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information: 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. We release the generated data at Github upon acceptance.<|reference_end|>
@article{hui2024toxicraft:,
    title={ToxiCraft: A Novel Framework for Synthetic Generation of Harmful
  Information},
    author={Zheng Hui, Zhaoxiao Guo, Hang Zhao, Juanyong Duan, Congrui Huang},
    journal={arXiv preprint arXiv:2409.14740},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14740},
    primaryClass={cs.CL cs.AI}
}