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Introducing SimpleQA

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.
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).

Introducing SimpleQA

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.
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).
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