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)