3 papers
cs.CL2025
ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information
Zheng Hui, Zhaoxiao Guo, Hang Zhao +2
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…
cs.CL2025
ToxiLab: How Well Do Open-Source LLMs Generate Synthetic Toxicity Data?
Zheng Hui, Zhaoxiao Guo, Hang Zhao +5
Effective toxic content detection relies heavily on high-quality and diverse data, which serve as the foundation for robust content moderation models. Synthetic data has become a c…
cs.CL2024
STAND-Guard: A Small Task-Adaptive Content Moderation Model
Minjia Wang, Pingping Lin, Siqi Cai +5
Content moderation, the process of reviewing and monitoring the safety of generated content, is important for development of welcoming online platforms and responsible large langua…