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)

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