126 lines
3.5 KiB
Markdown
126 lines
3.5 KiB
Markdown
# MuSR
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## 概述
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MuSR（Multistep Soft Reasoning，多步软推理）是一个通过基于叙事的问题来评估复杂推理能力的基准测试。它包含谋杀谜题、物品放置和团队分配等场景，要求模型进行多步推理。
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## 任务描述
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- **任务类型**：复杂推理（多项选择题）
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- **输入**：包含问题和选项的叙事场景
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- **输出**：正确答案的字母（A-F）
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- **领域**：谋杀谜题、物品追踪、团队分配
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## 主要特点
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- 基于叙事的推理问题
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- 需要多步逻辑推理
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- 三个不同的推理领域
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- 测试约束满足与演绎能力
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- 上下文较长，需仔细推理
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## 评估说明
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- 默认配置使用 **0-shot** 评估
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- 使用思维链（Chain-of-Thought, CoT）提示
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- 包含三个子集：`murder_mysteries`、`object_placements`、`team_allocation`
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- 使用简单准确率（accuracy）作为指标
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- 是一个具有挑战性的基准，要求仔细阅读
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `musr` |
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| **数据集ID** | [AI-ModelScope/MuSR](https://modelscope.cn/datasets/AI-ModelScope/MuSR/summary) |
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| **论文** | N/A |
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| **标签** | `MCQ`, `Reasoning` |
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| **指标** | `acc` |
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| **默认示例数** | 0-shot |
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| **评估划分** | `test` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 756 |
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| 提示词长度（平均） | 4891.57 字符 |
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| 提示词长度（最小/最大） | 2812 / 7537 字符 |
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**各子集统计数据：**
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| 子集 | 样本数 | 提示平均长度 | 提示最小长度 | 提示最大长度 |
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|--------|---------|-------------|------------|------------|
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| `murder_mysteries` | 250 | 5743.1 | 4056 | 7537 |
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| `object_placements` | 256 | 5294.0 | 3735 | 7525 |
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| `team_allocation` | 250 | 3627.93 | 2812 | 4351 |
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## 样例示例
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**子集**: `murder_mysteries`
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```json
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{
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"input": [
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{
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"id": "5ec1a7bd",
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"content": "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B. Think step by step before answering.\n\nIn an adrenaline inducing ... [TRUNCATED] ... and wronged, over and over, at the same sight. It was quite a sight. \n\nWinston, shuffling back to the station, was left with one thought - Looks like Mackenzie had quite an eventful week.\n\nWho is the most likely murderer?\n\nA) Mackenzie\nB) Ana"
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}
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],
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"choices": [
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"Mackenzie",
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"Ana"
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],
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"target": "A",
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"id": 0,
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"group_id": 0
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}
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```
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*注：部分内容为显示目的已截断。*
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## 提示模板
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**提示模板：**
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```text
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Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.
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{question}
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{choices}
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```
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## 使用方法
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### 使用命令行（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 musr \
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--limit 10 # 正式评估时请删除此行
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```
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### 使用 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=['musr'],
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dataset_args={
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'musr': {
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# subset_list: ['murder_mysteries', 'object_placements', 'team_allocation'] # 可选，用于评估特定子集
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}
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},
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limit=10, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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``` |