ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917406278942720 |
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| author | Li, Boyang Shou, Hongzhe Liang, Yuanyuan Zhang, Jingbin Zhou, Fang |
| author_facet | Li, Boyang Shou, Hongzhe Liang, Yuanyuan Zhang, Jingbin Zhou, Fang |
| contents | Existing Chinese toxic content detection methods mainly target sentence-level classification but often fail to provide readable and contiguous toxic evidence spans. We propose \textbf{ToxiTrace}, an explainability-oriented method for BERT-style encoders with three components: (1) \textbf{CuSA}, which refines encoder-derived saliency cues into fine-grained toxic spans with lightweight LLM guidance; (2) \textbf{GCLoss}, a gradient-constrained objective that concentrates token-level saliency on toxic evidence while suppressing irrelevant activations; and (3) \textbf{ARCL}, which constructs sample-specific contrastive reasoning pairs to sharpen the semantic boundary between toxic and non-toxic content. Experiments show that ToxiTrace improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent, human-readable explanations. We have released the model at https://huggingface.co/ArdLi/ToxiTrace. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_12321 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection Li, Boyang Shou, Hongzhe Liang, Yuanyuan Zhang, Jingbin Zhou, Fang Computation and Language Existing Chinese toxic content detection methods mainly target sentence-level classification but often fail to provide readable and contiguous toxic evidence spans. We propose \textbf{ToxiTrace}, an explainability-oriented method for BERT-style encoders with three components: (1) \textbf{CuSA}, which refines encoder-derived saliency cues into fine-grained toxic spans with lightweight LLM guidance; (2) \textbf{GCLoss}, a gradient-constrained objective that concentrates token-level saliency on toxic evidence while suppressing irrelevant activations; and (3) \textbf{ARCL}, which constructs sample-specific contrastive reasoning pairs to sharpen the semantic boundary between toxic and non-toxic content. Experiments show that ToxiTrace improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent, human-readable explanations. We have released the model at https://huggingface.co/ArdLi/ToxiTrace. |
| title | ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.12321 |