ARC

Overview

ARC (AI2 Reasoning Challenge) is a benchmark designed to evaluate science question answering capabilities of AI models. It consists of multiple-choice science questions from grade 3 to grade 9, divided into an Easy set and a Challenge set based on difficulty.

Task Description

Task Type: Multiple-Choice Science Question Answering
Input: Science question with 3-5 answer choices
Output: Correct answer letter (A, B, C, D, or E)
Difficulty Levels: ARC-Easy and ARC-Challenge

Key Features

7,787 science questions from standardized tests (grades 3-9)
ARC-Easy: Questions answerable by retrieval or word co-occurrence
ARC-Challenge: Questions requiring deeper reasoning
Questions cover physics, chemistry, biology, and earth science
Designed to test both factual knowledge and reasoning

Evaluation Notes

Default configuration uses 0-shot evaluation
Two subsets available: ARC-Easy and ARC-Challenge
Challenge set is commonly used for leaderboard comparisons
Supports few-shot evaluation with train split examples

Properties

Property	Value
Benchmark Name	`arc`
Dataset ID	allenai/ai2_arc
Paper	N/A
Tags	`MCQ`, `Reasoning`
Metrics	`acc`
Default Shots	0-shot
Evaluation Split	`test`
Train Split	`train`

Data Statistics

Metric	Value
Total Samples	3,548
Prompt Length (Mean)	424.43 chars
Prompt Length (Min/Max)	253 / 1157 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`ARC-Easy`	2,376	409.9	253	1157
`ARC-Challenge`	1,172	453.91	253	1111

Sample Example

Subset: ARC-Easy

{
  "input": [
    {
      "id": "76edd7a5",
      "content": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D.\n\nWhich statement best explains why photosynthesis is the foundation of most food webs?\n\nA) Sunlight is the source of energy for nearly all ecosystems.\nB) Most ecosystems are found on land instead of in water.\nC) Carbon dioxide is more available than other gases.\nD) The producers in all ecosystems are plants."
    }
  ],
  "choices": [
    "Sunlight is the source of energy for nearly all ecosystems.",
    "Most ecosystems are found on land instead of in water.",
    "Carbon dioxide is more available than other gases.",
    "The producers in all ecosystems are plants."
  ],
  "target": "A",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "id": "Mercury_417466"
  }
}

Prompt Template

Prompt Template:

Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.

{question}

{choices}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets arc \
    --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=['arc'],
    dataset_args={
        'arc': {
            # subset_list: ['ARC-Easy', 'ARC-Challenge']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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ARC