110 lines
3.0 KiB
Markdown
110 lines
3.0 KiB
Markdown
# General-MCQ
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## Overview
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General-MCQ is a customizable multiple-choice question answering benchmark for evaluating language models. It supports flexible data formats and variable number of answer choices.
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## Task Description
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- **Task Type**: Multiple-Choice Question Answering
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- **Input**: Question with 2-10 answer choices (A through J)
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- **Output**: Selected answer choice(s)
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- **Flexibility**: Supports custom datasets via local files, single or multiple correct answers
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## Key Features
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- Flexible number of choices (A through J)
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- Custom dataset support via local file loading
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- Chinese single-/multiple-answer prompt templates (optional CoT variants)
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- Configurable few-shot examples
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- Accuracy-based evaluation
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation with single-answer template
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- Set `extra_params.multiple_correct=True` to evaluate questions with multiple correct answers
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- Set `extra_params.use_cot=True` to switch to chain-of-thought prompt templates
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- Primary metric: **Accuracy**
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- Train split: **dev**, Eval split: **val**
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- See [User Guide](https://evalscope.readthedocs.io/en/latest/advanced_guides/custom_dataset/llm.html#mcq) for dataset format
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `general_mcq` |
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| **Dataset ID** | `general_mcq` |
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| **Paper** | N/A |
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| **Tags** | `Custom`, `MCQ` |
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| **Metrics** | `acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `val` |
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| **Train Split** | `dev` |
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## Data Statistics
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*Statistics not available.*
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## Sample Example
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*Sample example not available.*
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## Prompt Template
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**Prompt Template:**
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```text
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回答下面的单项选择题，请选出其中的正确答案。你的回答的全部内容应该是这样的格式："答案：[LETTER]"（不带引号），其中 [LETTER] 是 {letters} 中的一个。
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问题：{question}
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选项：
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{choices}
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```
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## Extra Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `multiple_correct` | `bool` | `False` | Whether the dataset contains questions with multiple correct answers. When True, switches to the multiple-answer prompt template and parser, and requires the `answer` field to be a list of letters (e.g., ["A", "C"]). |
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| `use_cot` | `bool` | `False` | Whether to use the chain-of-thought (CoT) prompt template variant. |
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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 general_mcq \
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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=['general_mcq'],
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dataset_args={
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'general_mcq': {
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# extra_params: {} # uses default extra parameters
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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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