132 lines
3.5 KiB
Markdown
132 lines
3.5 KiB
Markdown
# MuSR
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## Overview
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MuSR (Multistep Soft Reasoning) is a benchmark for evaluating complex reasoning abilities through narrative-based problems. It includes murder mysteries, object placements, and team allocation scenarios requiring multi-step inference.
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## Task Description
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- **Task Type**: Complex Reasoning (Multiple-Choice)
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- **Input**: Narrative scenario with question and answer choices
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- **Output**: Correct answer letter (A-F)
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- **Domains**: Murder mysteries, object tracking, team allocation
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## Key Features
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- Narrative-based reasoning problems
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- Requires multi-step logical inference
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- Three distinct reasoning domains
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- Tests constraint satisfaction and deduction
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- Longer context requiring careful reasoning
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Uses Chain-of-Thought (CoT) prompting
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- Three subsets: `murder_mysteries`, `object_placements`, `team_allocation`
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- Simple accuracy metric
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- Challenging benchmark requiring careful reading
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `musr` |
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| **Dataset ID** | [AI-ModelScope/MuSR](https://modelscope.cn/datasets/AI-ModelScope/MuSR/summary) |
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| **Paper** | N/A |
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| **Tags** | `MCQ`, `Reasoning` |
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| **Metrics** | `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 | 756 |
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| Prompt Length (Mean) | 4891.57 chars |
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| Prompt Length (Min/Max) | 2812 / 7537 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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| `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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## Sample Example
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**Subset**: `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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*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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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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## 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 musr \
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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=['musr'],
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dataset_args={
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'musr': {
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# subset_list: ['murder_mysteries', 'object_placements', 'team_allocation'] # 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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