ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection

TL;DR

ToxiTrace is a novel explainability method for Chinese toxicity detection that enhances toxic span identification and provides human-readable explanations while maintaining classification accuracy.

Contribution

It introduces a gradient-aligned training framework with three components to improve toxic span extraction and explanation quality in BERT-based models.

Findings

Improves toxic span extraction accuracy.

Enhances the coherence and readability of explanations.

Maintains efficient encoder-based inference.

Abstract

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.