# Cpsdbench Eval

> Evaluates LLMs on Chinese public security domain tasks including text classification, information extraction, question answering, and text generation. Probes domain-specific accuracy, reliability, and contextual understanding in high-stakes law enforcement scenarios. Use when the user wants to benchmark on Weibo Sentiment Analysis, Rumor Detection, Telecommunication Fraud Detection, Drug-Related Case Reports, Public Security Case Reading Comprehension, Public Security Case Summary, or asks about evaluating this task. Reports F1-Score.

- Skill: `qhjqhj00/cpsdbench-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/cpsdbench-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/cpsdbench-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/cpsdbench-eval

---


# cpsdbench-eval

> CPSDBench: A Large Language Model Evaluation Benchmark and Baseline for Chinese Public Security Domain — Tong et al. (2024) (arXiv:2402.07234, 2024)

## What this evaluates

Evaluates LLMs on Chinese public security domain tasks including text classification, information extraction, question answering, and text generation. Probes domain-specific accuracy, reliability, and contextual understanding in high-stakes law enforcement scenarios.

## Datasets

- **Weibo Sentiment Analysis** — total 600; splits: test (-1)
- **Rumor Detection** — total 400; splits: test (-1)
- **Telecommunication Fraud Detection** — total 400; splits: test (-1)
- **Drug-Related Case Reports** — total ?; splits: test (-1)
- **Public Security Case Reading Comprehension** — total ?; splits: test (-1)
- **Public Security Case Summary** — total ?; splits: test (-1)

## Metrics

- `Accuracy` — range: [0, 1]
  - Calculated as (TP+TN)/(TP+TN+FP+FN). Represents the proportion of correctly classified instances out of the total.
- `Precision` — range: [0, 1]
  - Calculated as TP/(TP+FP). Measures the proportion of positive predictions that are actually correct.
- `Recall` — range: [0, 1]
  - Calculated as TP/(TP+FN). Measures the proportion of actual positives that are correctly identified.
- `F1-Score` **(primary)** — range: [0, 1]
  - Harmonic mean of Precision and Recall: 2 * (Precision * Recall) / (Precision + Recall). Standard for classification and IE tasks.
- `Hybrid IE Metric` — range: [0, 1]
  - A two-stage score combining exact match and fuzzy match. Exact match is 1 if pred==gold else 0. Fuzzy match uses Levenshtein distance; if distance ≤ threshold, score is 1. Final score is a weighted sum of exact and fuzzy scores. High-precision entities (names, amounts) use exact match only.

## Input / output format

**Input**: Chinese text instances from public security scenarios (e.g., social media posts, case reports, fraud messages, legal questions), wrapped in task-specific prompts containing role definition, task description, input specifications, and operational constraints.

**Output**: Task-dependent outputs: discrete class labels for classification; structured entity/relation tuples for information extraction; natural language yes/no or open-ended answers for question answering; and coherent summary paragraphs for text generation.

## Scoring recipe

```python
def compute_metrics(preds, golds, task_type):
    if task_type == 'classification':
        acc = sum(p == g for p, g in zip(preds, golds)) / len(golds)
        return acc
    elif task_type == 'ie':
        exact_scores = []
        fuzzy_scores = []
        for p, g in zip(preds, golds):
            exact = 1.0 if p == g else 0.0
            exact_scores.append(exact)
            if exact == 0:
                lev = levenshtein_distance(p, g)
                fuzzy_scores.append(1.0 if lev <= THRESHOLD else 0.0)
            else:
                fuzzy_scores.append(0.0)
        return 0.5 * sum(exact_scores) + 0.5 * sum(fuzzy_scores)
    return None
```

## Common pitfalls

- Exact match vs. semantic equivalence: LLM outputs may differ literally but be semantically correct (e.g., '9:00 am' vs 'around 9:00 am'). The hybrid metric accounts for this via Levenshtein distance.
- High-precision entities (names, amounts) in fraud/IE tasks strictly use exact match only, ignoring fuzzy matches, which can penalize minor phrasing variations.
- Dataset sizes are small (dozens to hundreds) due to commercial API costs, limiting statistical power and generalizability of results.

## Evidence (verbatim from paper)

> For text classification tasks, we have chosen Accuracy, Precision, Recall, and F1-Score as evaluation metrics. ... For information extraction tasks, the typical metrics are Precision, Recall, and F1-Score. ... we have designed a hybrid evaluation metric. It comprises two steps: firstly, calculating the exact match score between the LLM’s output and the label. Secondly, for predictions that are not exact matches, we calculate the Levenshtein distance... Finally, we obtain a comprehensive score by weighting these two types of scores.

## Citation

```bibtex
@misc{tong2024cpsdbench,
  title={CPSDBench: A Large Language Model Evaluation Benchmark and Baseline for Chinese Public Security Domain},
  author={Tong et al. (2024)},
  year={2024},
  note={arXiv:2402.07234}
}
```

- arXiv: 2402.07234