RACE

Overview

RACE (ReAding Comprehension from Examinations) is a large-scale reading comprehension benchmark collected from Chinese middle school and high school English examinations. It tests comprehensive reading comprehension abilities.

Task Description

Task Type: Reading Comprehension (Multiple-Choice)
Input: Article passage with question and 4 answer choices
Output: Correct answer letter (A, B, C, or D)
Difficulty Levels: Middle school and High school

Key Features

28,000+ passages with 100,000 questions
Real examination questions for authentic difficulty
Two subsets: middle (easier) and high (harder)
Tests various comprehension skills (inference, vocabulary, main idea, etc.)
Diverse article topics and question types

Evaluation Notes

Default configuration uses 3-shot examples
Maximum few-shot number is 3 (context length consideration)
Uses Chain-of-Thought (CoT) prompting
Two subsets available: high and middle
Evaluates on test split

Properties

Property	Value
Benchmark Name	`race`
Dataset ID	evalscope/race
Paper	N/A
Tags	`MCQ`, `Reasoning`
Metrics	`acc`
Default Shots	3-shot
Evaluation Split	`test`
Train Split	`train`

Data Statistics

Metric	Value
Total Samples	4,934
Prompt Length (Mean)	7217.34 chars
Prompt Length (Min/Max)	3685 / 11131 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`high`	3,498	8279.47	6585	11131
`middle`	1,436	4630.07	3685	6032

Sample Example

Subset: high

{
  "input": [
    {
      "id": "706382e9",
      "content": "Here are some examples of how to answer similar questions:\n\nArticle:\nLast week I talked with some of my students about what they wanted to do after they graduated, and what kind of job prospects  they thought they had.\nGiven that I teach stud ... [TRUNCATED] ... I owe my life to her,\" said Nancy with tears.\nQuestion:\nWhat did Nancy try to do before she fell over?\n\nA) Measure the depth of the river\nB) Look for a fallen tree trunk\nC) Protect her cows from being drowned\nD) Run away from the flooded farm"
    }
  ],
  "choices": [
    "Measure the depth of the river",
    "Look for a fallen tree trunk",
    "Protect her cows from being drowned",
    "Run away from the flooded farm"
  ],
  "target": "C",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "example_id": "high19432.txt"
  }
}

Note: Some content was truncated for display.

Prompt Template

Prompt Template:

Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.

{question}

{choices}

Usage

Using CLI

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

run_task(task_cfg=task_cfg)

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