ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information

• URL: http://arxiv.org/abs/2409.14740v2
• Date: Mon, 14 Apr 2025 18:30:57 GMT
• Title: ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information
• Authors: Zheng Hui, Zhaoxiao Guo, Hang Zhao, Juanyong Duan, Congrui Huang,
• Abstract summary: Toxicraft is a novel framework for synthesizing datasets of harmful information.<n>With only a small amount of seed data, our framework can generate a wide variety of synthetic, yet remarkably realistic, examples of toxic information.
• Score: 30.333357539780287
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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