用大模型引导生成有方向性的有毒文本，提升分类鲁棒性
ToxiGAN: Toxic Data Augmentation via LLM-Guided Directional Adversarial Generation
- 通过双向训练+大模型中性样本引导，实现可控的毒性数据增强
- 在4个仇恨言论数据集上平均宏F1和仇恨类F1均领先现有方法
- 适合需要提升毒性分类器抗干扰能力的研究者使用
在可控且类别特定的前提下扩充有毒语言数据，对提升毒性分类的鲁棒性至关重要，但受限于监督信号不足与分布偏差。我们提出ToxiGAN，一种类别感知的文本增强框架，结合对抗生成与大语言模型（LLM）的语义引导。为解决传统GAN增强中的模式崩溃和语义漂移问题，ToxiGAN采用两阶段定向训练策略，并利用LLM生成的中性文本作为语义平衡剂。不同于将LLM视为静态生成器的做法，我们的方法动态选取中性样本以提供均衡引导。有毒样本被显式优化以远离这些中性样本，强化类别间的对比信号。在四个仇恨言论基准上的实验表明，ToxiGAN在宏F1和仇恨类F1上均取得最佳平均表现，显著优于传统及基于LLM的增强方法。消融与敏感性分析进一步验证了语义平衡剂和定向训练对提升分类器鲁棒性的有效性。
原文摘要 · Abstract (English)
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.
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