# Chinese Toxicity Detection Eval

> chinese-toxicity-detection-eval

- Skill: `qhjqhj00/chinese-toxicity-detection-eval` (Agent Skill)
- Install (CLI): `npx skillmds@latest add qhjqhj00/chinese-toxicity-detection-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/chinese-toxicity-detection-eval/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/qhjqhj00/chinese-toxicity-detection-eval

---


# chinese-toxicity-detection-eval

> ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection — Li et al. (2026) (arXiv:2604.12321, 2026)

## What this evaluates

This protocol evaluates models on Chinese toxicity detection across two tasks: binary sentence-level classification and fine-grained toxic span extraction. It measures classification accuracy and precision/recall, while also assessing the model's ability to extract contiguous, human-readable toxic spans and the faithfulness of those explanations via confidence masking.

## Datasets

- **COLD** — total 32480; splits: test (5323)
- **ToxiCN** — total 12011; splits: test (2411)
- **CNTP** — total 2533; splits: test (2533)

## Metrics

- `Accuracy (Acc)` — range: [0, 1]
  - Proportion of correctly classified instances out of the total number of instances.
- `Precision (P)` — range: [0, 1]
  - Ratio of true positive predictions to all positive predictions (TP / (TP + FP)).
- `Recall (R)` — range: [0, 1]
  - Ratio of true positive predictions to all actual positive instances (TP / (TP + FN)).
- `F1` **(primary)** — range: [0, 1]
  - Harmonic mean of Precision and Recall: 2 * (P * R) / (P + R).
- `Macro-F1` — range: [0, 1]
  - Unweighted mean of F1 scores computed per class, treating all classes equally.
- `Overlap F1` **(primary)** — range: [0, 1]
  - Span-level F1 where a prediction is correct if its overlap with the gold span exceeds 50%. Precision and Recall are computed over these binary correct/incorrect judgments.
- `Character-level F1` — range: [0, 1]
  - Token-level F1 score computed by matching extracted character spans against ground-truth character spans.
- `IoU` — range: [0, 1]
  - Intersection over Union of character indices between predicted and gold spans.

## Input / output format

**Input**: Chinese text sentences (for classification) or sentences with ground-truth toxic spans (for span extraction evaluation).

**Output**: Binary label (toxic/non-toxic) for classification; contiguous toxic span(s) or character-level attribution scores for extraction.

## Scoring recipe

```python
def compute_classification_metrics(preds, golds):
    tp = fp = fn = 0
    for p, g in zip(preds, golds):
        if p == 1 and g == 1: tp += 1
        elif p == 1 and g == 0: fp += 1
        elif p == 0 and g == 1: fn += 1
    acc = (tp + (len(preds) - tp - fp)) / len(preds)
    p = tp / (tp + fp) if (tp + fp) > 0 else 0
    r = tp / (tp + fn) if (tp + fn) > 0 else 0
    f1 = 2 * p * r / (p + r) if (p + r) > 0 else 0
    return acc, p, r, f1

def compute_overlap_f1(pred_spans, gold_spans):
    correct = 0
    for p, g in zip(pred_spans, gold_spans):
        if len(g) == 0: continue
        overlap = len(set(p) & set(g)) / len(g)
        if overlap > 0.5: correct += 1
    return correct / len(gold_spans)
```

## Common pitfalls

- Zero-shot LLMs perform poorly on Chinese toxicity tasks; fair comparison requires fine-tuning or instruction tuning, as noted in the experimental setup.
- Span extraction uses a strict >50% overlap threshold, which can penalize partial matches even if they capture the core toxic phrase.
- Character-level metrics in Chinese often yield fragmented spans; the protocol uses BiCSE to enforce contiguous span extraction for better evaluation.

## Evidence (verbatim from paper)

> To evaluate toxic content detection performance, we used five widely adopted metrics: Accuracy ($Acc$), Recall ($R$), Precision ($P$), $F_{1}$ and Macro-$F_{1}$ Score.

## Citation

```bibtex
@misc{li2026toxitrace,
  title={ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection},
  author={Li et al. (2026)},
  year={2026},
  note={arXiv:2604.12321}
}
```

- arXiv: 2604.12321