121 lines
2.8 KiB
Markdown
121 lines
2.8 KiB
Markdown
# SimpleQA
|
|
|
|
|
|
## Overview
|
|
|
|
SimpleQA is a benchmark by OpenAI designed to evaluate language models' ability to answer short, fact-seeking questions accurately. It focuses on measuring factual accuracy with clear grading criteria for correct, incorrect, and not-attempted answers.
|
|
|
|
## Task Description
|
|
|
|
- **Task Type**: Factual Question Answering
|
|
- **Input**: Simple factual question
|
|
- **Output**: Concise factual answer
|
|
- **Grading**: CORRECT, INCORRECT, or NOT_ATTEMPTED
|
|
|
|
## Key Features
|
|
|
|
- Short, fact-seeking questions with unambiguous answers
|
|
- Clear grading criteria for accuracy evaluation
|
|
- Distinguishes between incorrect answers and abstentions
|
|
- Uses LLM-as-judge for semantic answer comparison
|
|
- Tests factual knowledge and calibration
|
|
|
|
## Evaluation Notes
|
|
|
|
- Default configuration uses **0-shot** evaluation
|
|
- Uses LLM judge for answer grading (semantic matching)
|
|
- Three-way classification: is_correct, is_incorrect, is_not_attempted
|
|
- Allows hedging if correct information is included
|
|
- Tests models' ability to admit uncertainty appropriately
|
|
|
|
|
|
## Properties
|
|
|
|
| Property | Value |
|
|
|----------|-------|
|
|
| **Benchmark Name** | `simple_qa` |
|
|
| **Dataset ID** | [evalscope/SimpleQA](https://modelscope.cn/datasets/evalscope/SimpleQA/summary) |
|
|
| **Paper** | N/A |
|
|
| **Tags** | `Knowledge`, `QA` |
|
|
| **Metrics** | `is_correct`, `is_incorrect`, `is_not_attempted` |
|
|
| **Default Shots** | 0-shot |
|
|
| **Evaluation Split** | `test` |
|
|
|
|
|
|
## Data Statistics
|
|
|
|
| Metric | Value |
|
|
|--------|-------|
|
|
| Total Samples | 4,326 |
|
|
| Prompt Length (Mean) | 118.47 chars |
|
|
| Prompt Length (Min/Max) | 48 / 403 chars |
|
|
|
|
## Sample Example
|
|
|
|
**Subset**: `default`
|
|
|
|
```json
|
|
{
|
|
"input": [
|
|
{
|
|
"id": "9dd57f4c",
|
|
"content": "Answer the question:\n\nWho received the IEEE Frank Rosenblatt Award in 2010?"
|
|
}
|
|
],
|
|
"target": "Michio Sugeno",
|
|
"id": 0,
|
|
"group_id": 0,
|
|
"metadata": {
|
|
"topic": "Science and technology",
|
|
"answer_type": "Person",
|
|
"urls": [
|
|
"https://en.wikipedia.org/wiki/IEEE_Frank_Rosenblatt_Award",
|
|
"https://ieeexplore.ieee.org/author/37271220500",
|
|
"https://en.wikipedia.org/wiki/IEEE_Frank_Rosenblatt_Award",
|
|
"https://www.nxtbook.com/nxtbooks/ieee/awards_2010/index.php?startid=21#/p/20"
|
|
]
|
|
}
|
|
}
|
|
```
|
|
|
|
## Prompt Template
|
|
|
|
**Prompt Template:**
|
|
```text
|
|
Answer the question:
|
|
|
|
{question}
|
|
```
|
|
|
|
## Usage
|
|
|
|
### Using CLI
|
|
|
|
```bash
|
|
evalscope eval \
|
|
--model YOUR_MODEL \
|
|
--api-url OPENAI_API_COMPAT_URL \
|
|
--api-key EMPTY_TOKEN \
|
|
--datasets simple_qa \
|
|
--limit 10 # Remove this line for formal evaluation
|
|
```
|
|
|
|
### Using Python
|
|
|
|
```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=['simple_qa'],
|
|
limit=10, # Remove this line for formal evaluation
|
|
)
|
|
|
|
run_task(task_cfg=task_cfg)
|
|
```
|
|
|
|
|