13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
163 lines
10 KiB
Markdown
163 lines
10 KiB
Markdown
# PLawBench
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## Overview
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PLawBench is a rubric-based benchmark that evaluates large language models on real-world Chinese legal practice.
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It mirrors the workflow of a practising lawyer across three hierarchical levels: eliciting facts during a public
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legal consultation, analysing a case with structured legal reasoning, and drafting professional legal documents.
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Every item ships with a rubric annotated by legal experts, and grading is performed by an LLM judge against that
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rubric rather than against a single reference answer.
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## Task Description
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- **Task Type**: Open-ended Chinese legal generation graded with expert rubrics
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- **Input**: A client statement, or a case description plus a legal question
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- **Output**: A question list, a structured case analysis, or a full legal document
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- **Domain**: Chinese legal practice (personal affairs, marriage and family, corporate governance, intellectual
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property, criminal and civil litigation, cross-border matters, labour, environmental safety, and more)
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## Key Features
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- 280 samples split into four subsets, one per PLawBench task:
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- `case_analysis` (250): case analysis scored on four dimensions — conclusion, case facts, reasoning, and cited
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statutes. Answers must follow the 【结论】/【案件事实】/【推理过程】/【法条依据】 structure.
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- `legal_consultation` (18): the model plays a lawyer and must produce 10-25 verifiable follow-up questions that
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surface the facts the client omitted or distorted.
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- `plaintiff_statement` (6): drafting a statement of complaint from the client's account.
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- `defendant_statement` (6): drafting a statement of defense from the client's account and the opposing complaint.
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- Client statements are deliberately vague, emotional, or misleading, so models must detect traps instead of
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restating the client's claims.
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- Task prompts and judge prompts are ported verbatim from the official release, and the `case_analysis` rubric
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retains its per-dimension point allocation.
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## Evaluation Notes
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- Requires an LLM judge: set `judge.strategy='llm'` (or `'auto'`, which enables the judge for this benchmark)
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and provide `judge.models`. `judge.strategy='rule'` is not supported.
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- Metrics are point ratios in `[0, 1]`. `acc` is reported for every subset; `case_analysis` additionally reports
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`conclusion_acc`, `fact_acc`, `reasoning_acc`, and `law_acc`. These map one-to-one onto the official leaderboard
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columns: `legal_consultation` is Task1, `case_analysis` is Task2-Avg with its four dimensions, and the two
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drafting subsets are Task3-Plaintiff and Task3-Defendant.
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- Compare per-subset scores, not the `OVERALL` row. `OVERALL` is a per-sample mean, so `case_analysis` dominates it
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(250 of 280 samples). The paper's `Overall` column is an equal-weighted mean of the three task scores, which
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matches its published table far more closely (mean absolute error 0.72 versus 2.87 for a sample-weighted mean,
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fitted across the 24 models in the official ranking).
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- Rubric point totals come from the dataset, not from the judge output, and awarded points are clamped into
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`[0, max_points]`, so a judge that mis-reports the denominator cannot distort the score.
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- The judge output template for `case_analysis` is repaired relative to the official script, which ships malformed
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JSON and pins the conclusion section to zero points; every section is graded on its rubric allocation here.
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- The judge model's transport retry policy is configured through its `generation_config`. A reply that still
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fails the output contract is unavailable and excluded rather than silently scored as zero.
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- Case-analysis judging returns a long per-item breakdown. Give the judge a generous `max_tokens`
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(for example 8192) in `judge.models[].generation_config`.
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- The drafting subsets ask for a 2,500-3,000 character legal document, so the evaluated model also needs a generous
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`generation_config.max_tokens`. A truncated filing is graded as an incomplete document and scores near zero, which
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depresses Task3 for reasons unrelated to legal ability.
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Resources: [GitHub](https://github.com/skylenage/PLawbench) |
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[Dataset](https://modelscope.cn/datasets/evalscope/PLawBench)
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `plawbench` |
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| **Dataset ID** | [evalscope/PLawBench](https://modelscope.cn/datasets/evalscope/PLawBench/summary) |
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| **Paper** | [Paper](https://github.com/skylenage/PLawbench) |
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| **Tags** | `Chinese`, `Knowledge`, `QA`, `Reasoning` |
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| **Metrics** | `accuracy`, `conclusion_acc`, `fact_acc`, `reasoning_acc`, `law_acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 280 |
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| Prompt Length (Mean) | 2669.88 chars |
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| Prompt Length (Min/Max) | 1267 / 5890 chars |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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|--------|---------|-------------|------------|------------|
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| `case_analysis` | 250 | 2720.18 | 2137 | 4873 |
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| `legal_consultation` | 18 | 1851.94 | 1512 | 2611 |
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| `plaintiff_statement` | 6 | 1494.67 | 1267 | 2050 |
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| `defendant_statement` | 6 | 4203 | 2794 | 5890 |
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## Sample Example
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**Subset**: `case_analysis`
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```json
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{
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"input": [
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{
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"id": "06c8a1d7",
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"content": "\n## 角色\n\n你是一名具有十年以上执业经验的法律实务专家，精通中国现行法律法规与司法实践。你擅长将复杂的法律问题分解为清晰的逻辑模块，并严格依据“结论先行、事实为重、推理严密、依据支撑”的专业风格进行解答。\n\n## 核心要求\n\n1. 严格顺序：回答必须按照以下四部分顺序展开，并使用对应标题：\n【结论】\n【案件事实】\n【推理过程】\n【法条依据】\n2. 内容规范：\n结论：直接、明确，针对提问的核心争议点给出肯定或否定的判断。\n案件事实：基于用户提供的案情，简明、客观地摘录与法律判断 ... [TRUNCATED 1912 chars] ... 并赔偿精神损害抚慰金。庭审中查明，某摄影服务公司已完成除摄像外的其他服务项目；某文化传媒公司系独立法人，其工作人员在操作设备时存在重大过失。另查，某甲在签订合同时未特别声明婚礼录像的重要性，但合同附件中列有\"全程跟拍记录\"服务项目。某摄影服务公司辩称其仅需承担合同违约责任，精神损害赔偿缺乏依据。某文化传媒公司以非合同相对方为由拒绝承担责任。\n\n## 问题\n以【结论 + 案情简述 + 分析过程+依据法条】的逻辑回答以下问题：在上述案例中，某甲能否向某摄影服务公司主张精神损害赔偿？\n"
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}
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],
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"target": "",
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"id": 0,
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"group_id": 0,
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"subset_key": "case_analysis",
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"metadata": {
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"id": "case_analysis-1",
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"task": "case_analysis",
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"judge_type": "case_analysis",
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"category": "个人生活",
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"rubrics": "[{\"criterion\": \"【结论得分】\\n(+5分) 某甲有权向某摄影服务公司主张精神损害赔偿。\", \"points\": \"5\", \"tags\": \"结论得分\"}, {\"criterion\": \"【案情简述得分】\\n(+5分) 某甲与某摄影服务公司签订《婚庆服务合同》，并支付全款，合同附件明确包含\\\"全程跟拍记录\\\"服务项目。\\n(+5分) 婚礼当日，某摄影服务公司未经告知将摄像服务转包给文化传媒公司。\\n(+5分) 文化传媒公司工作室将录像全部丢失，未能交付原告。\\n(+ ... [TRUNCATED 762 chars] ... 人具有人身意义的特定物造成严重精神损害的，被侵权人有权请求精神损害赔偿。\\n（+5分）《最高人民法院关于确定民事侵权精神损害赔偿责任若干问题的解释》第五条\\n精神损害的赔偿数额根据以下因素确定：(一)侵权人的过错程度，但是法律另有规定的除外；(二)侵权行为的目的、方式、场合等具体情节；(三)侵权行为所造成的后果；(四)侵权人的获利情况；(五)侵权人承担责任的经济能力；(六)受理诉讼法院所在地的平均生活水平。\", \"points\": \"15\", \"tags\": \"法条依据得分\"}]",
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"max_points": 60,
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"prompt": "\n## 角色\n\n你是一名具有十年以上执业经验的法律实务专家，精通中国现行法律法规与司法实践。你擅长将复杂的法律问题分解为清晰的逻辑模块，并严格依据“结论先行、事实为重、推理严密、依据支撑”的专业风格进行解答。\n\n## 核心要求\n\n1. 严格顺序：回答必须按照以下四部分顺序展开，并使用对应标题：\n【结论】\n【案件事实】\n【推理过程】\n【法条依据】\n2. 内容规范：\n结论：直接、明确，针对提问的核心争议点给出肯定或否定的判断。\n案件事实：基于用户提供的案情，简明、客观地摘录与法律判断 ... [TRUNCATED 1912 chars] ... 并赔偿精神损害抚慰金。庭审中查明，某摄影服务公司已完成除摄像外的其他服务项目；某文化传媒公司系独立法人，其工作人员在操作设备时存在重大过失。另查，某甲在签订合同时未特别声明婚礼录像的重要性，但合同附件中列有\"全程跟拍记录\"服务项目。某摄影服务公司辩称其仅需承担合同违约责任，精神损害赔偿缺乏依据。某文化传媒公司以非合同相对方为由拒绝承担责任。\n\n## 问题\n以【结论 + 案情简述 + 分析过程+依据法条】的逻辑回答以下问题：在上述案例中，某甲能否向某摄影服务公司主张精神损害赔偿？\n"
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}
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}
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```
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*Note: Some content was truncated for display.*
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## Prompt Template
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**Prompt Template:**
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```text
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{question}
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```
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## Usage
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### Using CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets plawbench \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['plawbench'],
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dataset_args={
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'plawbench': {
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# subset_list: ['case_analysis', 'legal_consultation', 'plaintiff_statement'] # optional, evaluate specific subsets
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}
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},
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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