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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.14740 |
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| _version_ | 1866913793901068288 |
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| author | Hui, Zheng Guo, Zhaoxiao Zhao, Hang Duan, Juanyong Huang, Congrui |
| author_facet | Hui, Zheng Guo, Zhaoxiao Zhao, Hang Duan, Juanyong Huang, Congrui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14740 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information Hui, Zheng Guo, Zhaoxiao Zhao, Hang Duan, Juanyong Huang, Congrui Computation and Language Artificial Intelligence 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. |
| title | ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.14740 |