ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection

• URL: http://arxiv.org/abs/2604.12321v1
• Date: Tue, 14 Apr 2026 05:54:32 GMT
• Title: ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection
• Abstract summary: ToxiTrace is an explainability-oriented method for BERT-style encoders with three components.<n>ToxiTrace improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent, human-readable explanations.
• Score: 9.769395444384012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Detoxification for LLM: From Dataset Itself [69.09071947137926]
  Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself.<n>We attempt to detoxify directly on raw corpora with SoCD (Soft Contrastive Decoding), which guides an LLM to localize and rewrite toxic spans in raw data while preserving semantics.<n>On GPT2-XL, HSPD attains state-of-the-art detoxification, reducing Toxicity Probability (TP) from 0.42 to 0.18 and Maximum Expected Toxicity (EMT) from 0.43 to 0.20.
  arXiv  Detail & Related papers  (2026-04-21T06:12:29Z)
• Unveiling Covert Toxicity in Multimodal Data via Toxicity Association Graphs: A Graph-Based Metric and Interpretable Detection Framework [58.01529356381494]
  We propose a novel detection framework based on Toxicity Association Graphs (TAGs)<n>We introduce the first quantifiable metric for hidden toxicity, the Multimodal Toxicity Covertness (MTC)<n>Our approach enables precise identification of covert toxicity while preserving full interpretability of the decision-making process.
  arXiv  Detail & Related papers  (2026-02-03T08:54:25Z)
• Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification [73.77171973106567]
  Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content.<n>Traditional methods fail to eliminate underlying toxic regions in parameters, leaving models vulnerable to adversarial attacks.<n>We propose GLOSS, a lightweight method that mitigates toxicity by identifying and eliminating this global subspace from FFN parameters.
  arXiv  Detail & Related papers  (2026-01-09T09:34:53Z)
• Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective [104.09817371557476]
  Large language models (LLMs) have achieved impressive results across a range of natural language processing tasks.<n>Their potential to generate harmful content has raised serious safety concerns.<n>We introduce three novel multi-label benchmarks for toxicity detection.
  arXiv  Detail & Related papers  (2025-10-16T06:50:33Z)
• Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing [77.75609817898035]
  Large Language Models (LLMs) have demonstrated impressive performance across various tasks, yet they remain vulnerable to generating toxic content.<n>We propose textscAutoregressive textscReward textscGuided textscRe presentation textscEditing (ARGRE)<n>ARGRE explicitly models toxicity transitions within the latent representation space, enabling stable and precise reward-guided editing.
  arXiv  Detail & Related papers  (2025-09-24T03:40:32Z)
• <think> So let's replace this phrase with insult... </think> Lessons learned from generation of toxic texts with LLMs [60.169913160819]
  This paper explores the possibility of using synthetic toxic data as an alternative to human-generated data for training models for detoxification.<n>Experiments show that models fine-tuned on synthetic data consistently perform worse than those trained on human data.<n>The root cause is identified as a critical lexical diversity gap: LLMs generate toxic content using a small, repetitive vocabulary of insults that fails to capture the nuances and variety of human toxicity.
  arXiv  Detail & Related papers  (2025-09-10T07:48:24Z)
• ToXCL: A Unified Framework for Toxic Speech Detection and Explanation [3.803993344850168]
  ToXCL is a unified framework for the detection and explanation of implicit toxic speech. ToXCL achieves new state-of-the-art effectiveness, and outperforms baselines significantly.
  arXiv  Detail & Related papers  (2024-03-25T12:21:38Z)
• Unveiling the Implicit Toxicity in Large Language Models [77.90933074675543]
  The open-endedness of large language models (LLMs) combined with their impressive capabilities may lead to new safety issues when being exploited for malicious use. We show that LLMs can generate diverse implicit toxic outputs that are exceptionally difficult to detect via simply zero-shot prompting. We propose a reinforcement learning (RL) based attacking method to further induce the implicit toxicity in LLMs.
  arXiv  Detail & Related papers  (2023-11-29T06:42:36Z)
• Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks [18.44630180661091]
  Existing datasets lack fine-grained annotation of toxic types and expressions. It is crucial to introduce lexical knowledge to detect the toxicity of posts. In this paper, we facilitate the fine-grained detection of Chinese toxic language.
  arXiv  Detail & Related papers  (2023-05-08T03:50:38Z)
• Challenges in Automated Debiasing for Toxic Language Detection [81.04406231100323]
  Biased associations have been a challenge in the development of classifiers for detecting toxic language. We investigate recently introduced debiasing methods for text classification datasets and models, as applied to toxic language detection. Our focus is on lexical (e.g., swear words, slurs, identity mentions) and dialectal markers (specifically African American English)
  arXiv  Detail & Related papers  (2021-01-29T22:03:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.