SimpleQA#

Overview#

SimpleQA is a benchmark by OpenAI designed to evaluate language models’ ability to answer short, fact-seeking questions accurately. It focuses on measuring factual accuracy with clear grading criteria for correct, incorrect, and not-attempted answers.

Task Description#

  • Task Type: Factual Question Answering

  • Input: Simple factual question

  • Output: Concise factual answer

  • Grading: CORRECT, INCORRECT, or NOT_ATTEMPTED

Key Features#

  • Short, fact-seeking questions with unambiguous answers

  • Clear grading criteria for accuracy evaluation

  • Distinguishes between incorrect answers and abstentions

  • Uses LLM-as-judge for semantic answer comparison

  • Tests factual knowledge and calibration

Evaluation Notes#

  • Default configuration uses 0-shot evaluation

  • Uses LLM judge for answer grading (semantic matching)

  • Three-way classification: is_correct, is_incorrect, is_not_attempted

  • Allows hedging if correct information is included

  • Tests models’ ability to admit uncertainty appropriately

Properties#

Property

Value

Benchmark Name

simple_qa

Dataset ID

evalscope/SimpleQA

Paper

N/A

Tags

Knowledge, QA

Metrics

is_correct, is_incorrect, is_not_attempted

Default Shots

0-shot

Evaluation Split

test

Data Statistics#

Metric

Value

Total Samples

4,326

Prompt Length (Mean)

118.47 chars

Prompt Length (Min/Max)

48 / 403 chars

Sample Example#

Subset: default

{
  "input": [
    {
      "id": "9dd57f4c",
      "content": "Answer the question:\n\nWho received the IEEE Frank Rosenblatt Award in 2010?"
    }
  ],
  "target": "Michio Sugeno",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "topic": "Science and technology",
    "answer_type": "Person",
    "urls": [
      "https://en.wikipedia.org/wiki/IEEE_Frank_Rosenblatt_Award",
      "https://ieeexplore.ieee.org/author/37271220500",
      "https://en.wikipedia.org/wiki/IEEE_Frank_Rosenblatt_Award",
      "https://www.nxtbook.com/nxtbooks/ieee/awards_2010/index.php?startid=21#/p/20"
    ]
  }
}

Prompt Template#

Prompt Template:

Answer the question:

{question}

Usage#

Using CLI#

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets simple_qa \
    --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=['simple_qa'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)