PLawBench#

Overview#

PLawBench is a rubric-based benchmark that evaluates large language models on real-world Chinese legal practice. It mirrors the workflow of a practising lawyer across three hierarchical levels: eliciting facts during a public legal consultation, analysing a case with structured legal reasoning, and drafting professional legal documents. Every item ships with a rubric annotated by legal experts, and grading is performed by an LLM judge against that rubric rather than against a single reference answer.

Task Description#

  • Task Type: Open-ended Chinese legal generation graded with expert rubrics

  • Input: A client statement, or a case description plus a legal question

  • Output: A question list, a structured case analysis, or a full legal document

  • Domain: Chinese legal practice (personal affairs, marriage and family, corporate governance, intellectual property, criminal and civil litigation, cross-border matters, labour, environmental safety, and more)

Key Features#

  • 280 samples split into four subsets, one per PLawBench task:

    • case_analysis (250): case analysis scored on four dimensions — conclusion, case facts, reasoning, and cited statutes. Answers must follow the 【结论】/【案件事实】/【推理过程】/【法条依据】 structure.

    • legal_consultation (18): the model plays a lawyer and must produce 10-25 verifiable follow-up questions that surface the facts the client omitted or distorted.

    • plaintiff_statement (6): drafting a statement of complaint from the client’s account.

    • defendant_statement (6): drafting a statement of defense from the client’s account and the opposing complaint.

  • Client statements are deliberately vague, emotional, or misleading, so models must detect traps instead of restating the client’s claims.

  • Task prompts and judge prompts are ported verbatim from the official release, and the case_analysis rubric retains its per-dimension point allocation.

Evaluation Notes#

  • Requires an LLM judge: run with judge_strategy='llm' (or 'auto', which enables the judge for this benchmark) and provide judge_model_args. judge_strategy='rule' is not supported.

  • Metrics are point ratios in [0, 1]. acc is reported for every subset; case_analysis additionally reports conclusion_acc, fact_acc, reasoning_acc, and law_acc. These map one-to-one onto the official leaderboard columns: legal_consultation is Task1, case_analysis is Task2-Avg with its four dimensions, and the two drafting subsets are Task3-Plaintiff and Task3-Defendant.

  • Compare per-subset scores, not the OVERALL row. OVERALL is a per-sample mean, so case_analysis dominates it (250 of 280 samples). The paper’s Overall column is an equal-weighted mean of the three task scores, which matches its published table far more closely (mean absolute error 0.72 versus 2.87 for a sample-weighted mean, fitted across the 24 models in the official ranking).

  • Rubric point totals come from the dataset, not from the judge output, and awarded points are clamped into [0, max_points], so a judge that mis-reports the denominator cannot distort the score.

  • The judge output template for case_analysis is repaired relative to the official script, which ships malformed JSON and pins the conclusion section to zero points; every section is graded on its rubric allocation here.

  • Judge requests are retried up to judge_retries times when the response cannot be parsed; a sample that still fails is scored 0 and flagged via judge_failed in the review metadata.

  • Case-analysis judging returns a long per-item breakdown. Give the judge a generous max_tokens (for example 8192) in judge_model_args.generation_config.

  • The drafting subsets ask for a 2,500-3,000 character legal document, so the evaluated model also needs a generous generation_config.max_tokens. A truncated filing is graded as an incomplete document and scores near zero, which depresses Task3 for reasons unrelated to legal ability.

Resources: GitHub | Dataset

Properties#

Property

Value

Benchmark Name

plawbench

Dataset ID

evalscope/PLawBench

Paper

Paper

Tags

Chinese, Knowledge, QA, Reasoning

Metrics

accuracy, conclusion_acc, fact_acc, reasoning_acc, law_acc

Default Shots

0-shot

Evaluation Split

test

Data Statistics#

Metric

Value

Total Samples

280

Prompt Length (Mean)

2669.88 chars

Prompt Length (Min/Max)

1267 / 5890 chars

Per-Subset Statistics:

Subset

Samples

Prompt Mean

Prompt Min

Prompt Max

case_analysis

250

2720.18

2137

4873

legal_consultation

18

1851.94

1512

2611

plaintiff_statement

6

1494.67

1267

2050

defendant_statement

6

4203

2794

5890

Sample Example#

Subset: case_analysis

{
  "input": [
    {
      "id": "06c8a1d7",
      "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"
    }
  ],
  "target": "",
  "id": 0,
  "group_id": 0,
  "subset_key": "case_analysis",
  "metadata": {
    "id": "case_analysis-1",
    "task": "case_analysis",
    "judge_type": "case_analysis",
    "category": "个人生活",
    "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\"}]",
    "max_points": 60,
    "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"
  }
}

Note: Some content was truncated for display.

Prompt Template#

Prompt Template:

{question}

Extra Parameters#

Parameter

Type

Default

Description

judge_retries

int

3

Maximum attempts per rubric judge request before the sample is scored as 0.

Usage#

Using CLI#

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets plawbench \
    --limit 10  # Remove this line for formal evaluation

Using Python#

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['plawbench'],
    dataset_args={
        'plawbench': {
            # subset_list: ['case_analysis', 'legal_consultation', 'plaintiff_statement']  # optional, evaluate specific subsets
            # extra_params: {}  # uses default extra parameters
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)