Factuality is a complicated topic because it is hard to measure—evaluating the factuality of any given arbitrary claim is challenging, and language models can generate long completions that contain dozens of factual claims. In SimpleQA, we will focus on short, fact-seeking queries, which reduces the scope of the benchmark but makes measuring factuality much more tractable.

With SimpleQA, our goal was to create a dataset with the following properties:

1. High correctness. Reference answers to questions are supported by sources from two independent AI trainers, and questions were written in such a way that the predicted answers are easy to grade. 
2. Diversity. SimpleQA covers a wide range of topics, from science and technology to TV shows and video games.
3. Challenging for frontier models. Compared to older benchmarks such as TriviaQA⁠(opens in a new window) (2017) or NQ⁠(opens in a new window) (2019), which have become saturated, SimpleQA was created to be a greater challenge for frontier models (e.g., GPT‑4o scores less than 40%).
4. Good researcher UX. SimpleQA is intended to be fast and simple to run due to its concise questions and answers. Grading is also efficient whether through the OpenAI API or another frontier model API. Additionally, with 4,326 questions, SimpleQA should have relatively low variance as an evaluation benchmark.

We hired AI trainers to browse the web and create short, fact-seeking questions and corresponding answers. To be included in the dataset, each question had to meet a strict set of criteria: it must have a single, indisputable answer for easy grading; the answer to the question should not change over time; and most questions had to induce hallucinations from either GPT‑4o or GPT‑3.5. To further improve the quality of the dataset, a second, independent AI trainer answered each question without seeing the original response. Only questions where both AI trainers’ answers agreed were included.

As a final verification of quality, we had a third AI trainer answer a random sample of 1,000 questions from the dataset. We found that the third AI trainer’s answer matched the original agreed answers 94.4% of the time, with a 5.6% disagreement rate. We then manually inspected these examples, and found that 2.8% of the 5.6% of disagreements were due to grader false negatives or human errors from the third trainer (e.g., incomplete answers or misinterpreting sources), and the remaining 2.8% were due to real issues with the question (e.g., ambiguous questions, or different websites giving conflicting answers). Hence, we estimate the inherent error rate of this dataset to be approximately 3%.