ToxiGAN: 通过LLM引导的定向对抗生成进行有毒数据增强
ToxiGAN: Toxic Data Augmentation via LLM-Guided Directional Adversarial Generation
AI总结：
ToxiGAN通过LLM引导的定向对抗生成方法，提升有毒数据增强的可控性和类别特定性，有效增强分类器鲁棒性。
摘要：
在可控且类别特定的方式下增强有毒语言数据对于提高毒性分类的鲁棒性至关重要，但受限于有限的监督和分布偏斜仍具挑战性。我们提出了ToxiGAN，一个具有类别的文本增强框架，结合对抗生成与来自大型语言模型（LLMs）的语义指导。为解决基于GAN的增强中的常见问题，如模式崩溃和语义漂移，ToxiGAN引入了两步定向训练策略，并利用LLM生成的中性文本作为语义平衡剂。与以往将LLM视为静态生成器不同，我们的方法动态选择中性示例以提供平衡的指导。有毒样本被显式优化以远离这些示例，强化类别特定的对比信号。在四个仇恨言论基准测试中的实验表明，ToxiGAN在宏F1和仇恨F1的平均表现最强，持续优于传统和基于LLM的增强方法。消融分析和敏感性分析进一步确认了语义平衡和定向训练在增强分类器鲁棒性方面的益处。
英文摘要：
Augmenting toxic language data in a controllable and class-specific manner is crucial for improving robustness in toxicity classification, yet remains challenging due to limited supervision and distributional skew. We propose ToxiGAN, a class-aware text augmentation framework that combines adversarial generation with semantic guidance from large language models (LLMs). To address common issues in GAN-based augmentation such as mode collapse and semantic drift, ToxiGAN introduces a two-step directional training strategy and leverages LLM-generated neutral texts as semantic ballast. Unlike prior work that treats LLMs as static generators, our approach dynamically selects neutral exemplars to provide balanced guidance. Toxic samples are explicitly optimized to diverge from these exemplars, reinforcing class-specific contrastive signals. Experiments on four hate speech benchmarks show that ToxiGAN achieves the strongest average performance in both macro-F1 and hate-F1, consistently outperforming traditional and LLM-based augmentation methods. Ablation and sensitivity analyses further confirm the benefits of semantic ballast and directional training in enhancing classifier robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
