113 lines
2.6 KiB
Markdown
113 lines
2.6 KiB
Markdown
# ArenaHard
|
|
|
|
|
|
## Overview
|
|
|
|
ArenaHard is a challenging benchmark that evaluates language models through competitive pairwise comparison. Models are judged against a GPT-4 baseline on difficult tasks requiring reasoning, understanding, and generation capabilities.
|
|
|
|
## Task Description
|
|
|
|
- **Task Type**: Competitive Model Evaluation (Arena-style)
|
|
- **Input**: Challenging instruction/question
|
|
- **Output**: Model response compared against GPT-4-0314 baseline
|
|
- **Scoring**: Elo-based rating from pairwise battles
|
|
|
|
## Key Features
|
|
|
|
- 500 challenging user prompts
|
|
- Two-game battle system (A vs B and B vs A)
|
|
- Elo rating calculation for model ranking
|
|
- Tests reasoning, instruction-following, and generation
|
|
- High correlation with Chatbot Arena rankings
|
|
|
|
## Evaluation Notes
|
|
|
|
- Default configuration uses **0-shot** evaluation
|
|
- Uses LLM judge (default: gpt-4-1106-preview)
|
|
- Baseline model: gpt-4-0314 outputs
|
|
- Reports win rate and Elo-based scores
|
|
- Note: Style-controlled win rate not currently supported
|
|
|
|
|
|
## Properties
|
|
|
|
| Property | Value |
|
|
|----------|-------|
|
|
| **Benchmark Name** | `arena_hard` |
|
|
| **Dataset ID** | [AI-ModelScope/arena-hard-auto-v0.1](https://modelscope.cn/datasets/AI-ModelScope/arena-hard-auto-v0.1/summary) |
|
|
| **Paper** | N/A |
|
|
| **Tags** | `Arena`, `InstructionFollowing` |
|
|
| **Metrics** | `winrate` |
|
|
| **Default Shots** | 0-shot |
|
|
| **Evaluation Split** | `test` |
|
|
| **Aggregation** | `elo` |
|
|
|
|
|
|
## Data Statistics
|
|
|
|
| Metric | Value |
|
|
|--------|-------|
|
|
| Total Samples | 500 |
|
|
| Prompt Length (Mean) | 406.36 chars |
|
|
| Prompt Length (Min/Max) | 29 / 9140 chars |
|
|
|
|
## Sample Example
|
|
|
|
**Subset**: `default`
|
|
|
|
```json
|
|
{
|
|
"input": [
|
|
{
|
|
"id": "088243c7",
|
|
"content": "Use ABC notation to write a melody in the style of a folk tune."
|
|
}
|
|
],
|
|
"target": "X:1\nT:Untitled Folk Tune\nM:4/4\nL:1/8\nK:G\n|:G2A2|B2A2|G2E2|D4|E2F2|G2F2|E2C2|B,4|\nA2B2|c2B2|A2F2|E4|D2E2|F2E2|D2B,2|C4:|",
|
|
"id": 0,
|
|
"group_id": 0,
|
|
"metadata": {
|
|
"capability": "ABC Sequence Puzzles & Groups"
|
|
}
|
|
}
|
|
```
|
|
|
|
## Prompt Template
|
|
|
|
**Prompt Template:**
|
|
```text
|
|
{question}
|
|
```
|
|
|
|
## Usage
|
|
|
|
### Using CLI
|
|
|
|
```bash
|
|
evalscope eval \
|
|
--model YOUR_MODEL \
|
|
--api-url OPENAI_API_COMPAT_URL \
|
|
--api-key EMPTY_TOKEN \
|
|
--datasets arena_hard \
|
|
--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=['arena_hard'],
|
|
limit=10, # Remove this line for formal evaluation
|
|
)
|
|
|
|
run_task(task_cfg=task_cfg)
|
|
```
|
|
|
|
|