2026-07-08 08:57:50 +00:00

8.5 KiB

SuperGPQA

Overview

SuperGPQA is a large-scale multiple-choice question answering dataset designed to evaluate model generalization across diverse fields. It contains 26,000+ questions from 50+ fields, with each question featuring 10 answer options.

Task Description

  • Task Type: Multiple-Choice Knowledge Assessment
  • Input: Question with 10 answer choices (A-J)
  • Output: Correct answer letter
  • Domains: 50+ fields across Science, Engineering, Medicine, Economics, Law, etc.

Key Features

  • 26,000+ questions across 50+ academic fields
  • 10 options per question (more challenging than standard 4-choice)
  • Broad coverage including:
    • Science: Mathematics, Physics, Chemistry, Biology
    • Engineering: Computer Science, Electrical, Mechanical
    • Medicine: Clinical, Basic Medical, Pharmacy
    • Humanities: Philosophy, History, Literature
    • Social Sciences: Economics, Law, Sociology

Evaluation Notes

  • Default evaluation uses the train split (only available split)
  • Primary metric: Accuracy on multiple-choice questions
  • Supports 0-shot or 5-shot evaluation only
  • Uses Chain-of-Thought (CoT) prompting
  • Results can be grouped by field or discipline category

Properties

Property Value
Benchmark Name super_gpqa
Dataset ID m-a-p/SuperGPQA
Paper N/A
Tags Knowledge, MCQ
Metrics acc
Default Shots 0-shot
Evaluation Split train

Data Statistics

Metric Value
Total Samples 26,529
Prompt Length (Mean) 826.04 chars
Prompt Length (Min/Max) 294 / 8444 chars

Per-Subset Statistics:

Subset Samples Prompt Mean Prompt Min Prompt Max
Electronic Science and Technology 246 994.09 355 6149
Philosophy 347 880.01 357 3171
Traditional Chinese Medicine 268 691.78 339 2282
Applied Economics 723 779.14 348 5407
Mathematics 2,622 908.66 326 7946
Physics 2,845 936.31 348 6780
Clinical Medicine 1,218 830.98 334 4003
Computer Science and Technology 763 823.37 331 4409
Information and Communication Engineering 504 921.14 347 5594
Control Science and Engineering 190 1042.88 354 6016
Theoretical Economics 150 843.95 363 2432
Law 591 1220.98 366 4267
History 674 595.18 326 3707
Basic Medicine 567 690.19 320 2368
Education 247 695.45 355 1728
Materials Science and Engineering 289 834.37 359 2630
Electrical Engineering 556 891.89 385 4025
Systems Science 50 1214.34 403 2676
Power Engineering and Engineering Thermophysics 684 885.2 345 3093
Military Science 205 709.2 349 2839
Biology 1,120 786.49 337 4581
Business Administration 142 877.2 359 2219
Language and Literature 440 778.78 313 4331
Public Health and Preventive Medicine 292 670.73 348 3899
Political Science 65 700.06 362 2239
Chemistry 1,769 823.53 330 6109
Hydraulic Engineering 218 840.64 354 2504
Chemical Engineering and Technology 410 1035.03 325 3839
Pharmacy 278 642.21 355 2629
Geography 133 636.14 336 2514
Art Studies 603 565.14 338 3552
Architecture 162 647.03 345 2684
Forestry Engineering 100 707.91 366 2676
Public Administration 151 730.48 339 4242
Oceanography 200 801.77 332 2462
Journalism and Communication 207 550.58 341 1620
Nuclear Science and Technology 107 850.05 378 3467
Weapon Science and Technology 100 866.71 338 3090
Naval Architecture and Ocean Engineering 138 599.32 295 2320
Environmental Science and Engineering 189 779.83 333 2629
Transportation Engineering 251 778.94 362 3854
Geology 341 810.18 350 3293
Physical Oceanography 50 933.54 364 2176
Musicology 426 540.96 294 2909
Stomatology 132 792.47 386 2975
Aquaculture 56 497.89 339 1457
Mechanical Engineering 176 863.61 377 3684
Aeronautical and Astronautical Science and Technology 119 792.2 366 3178
Civil Engineering 358 831.25 343 2306
Mechanics 908 955.85 433 8444
Petroleum and Natural Gas Engineering 112 725.53 382 1882
Sociology 143 758.95 334 3241
Food Science and Engineering 109 600.39 343 1383
Agricultural Engineering 104 960.7 335 3178
Surveying and Mapping Science and Technology 168 729.06 340 2648
Metallurgical Engineering 255 828.58 373 2421
Library, Information and Archival Management 150 806.67 368 3502
Mining Engineering 100 865.04 379 5785
Astronomy 405 810.0 334 2925
Geological Resources and Geological Engineering 50 722 369 1554
Atmospheric Science 203 732.33 337 2455
Optical Engineering 376 777.42 366 2718
Animal Husbandry 103 621.8 361 1913
Geophysics 100 927.86 384 7376
Crop Science 145 662.8 394 1694
Management Science and Engineering 58 765.22 411 1832
Psychology 87 674.59 393 1973
Forestry 131 649.55 393 1995
Textile Science and Engineering 100 723.75 378 2124
Veterinary Medicine 50 602.5 362 1063
Instrument Science and Technology 50 759.36 375 1820
Physical Education 150 687.39 365 2156

Sample Example

Subset: Electronic Science and Technology

{
  "input": [
    {
      "id": "d0ceb797",
      "content": "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 A,B,C,D,E,F,G,H,I,J. Think step by step before answering.\n\nThe commo ... [TRUNCATED] ... oduct of A1 and A2's common-mode rejection ratios\nG) the size of A2's common-mode rejection ratio\nH) the size of A1's common-mode rejection ratio\nI) The difference in the common-mode rejection ratio of A1 and A2 themselves\nJ) input resistance"
    }
  ],
  "choices": [
    "the absolute value of the difference in the common-mode rejection ratio of A1 and A2 themselves",
    "all of the above",
    "the average of A1 and A2's common-mode rejection ratios",
    "the sum of A1 and A2's common-mode rejection ratios",
    "the product of A1 and A2's common-mode rejection ratios",
    "the square root of the product of A1 and A2's common-mode rejection ratios",
    "the size of A2's common-mode rejection ratio",
    "the size of A1's common-mode rejection ratio",
    "The difference in the common-mode rejection ratio of A1 and A2 themselves",
    "input resistance"
  ],
  "target": "I",
  "id": 0,
  "group_id": 0,
  "subset_key": "Electronic Science and Technology",
  "metadata": {
    "field": "Electronic Science and Technology",
    "discipline": "Engineering",
    "uuid": "a8390c754538493ba59055689b4482aa",
    "explanation": "The difference in the common-mode rejection ratio of A1 and A2 themselves"
  }
}

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 super_gpqa \
    --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=['super_gpqa'],
    dataset_args={
        'super_gpqa': {
            # subset_list: ['Electronic Science and Technology', 'Philosophy', 'Traditional Chinese Medicine']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)