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
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

{
  "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:

{question}

Usage

Using CLI

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

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)

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