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Measuring short-form factuality in large language models
Summary
This document introduces SimpleQA, a new benchmark for evaluating the factuality of large language models. The benchmark consists of over 4,000 short, fact-seeking questions designed to be challenging for advanced models, with a focus on ensuring a single, indisputable answer. The authors argue that SimpleQA is a valuable tool for assessing whether models "know what they know", meaning their ability to correctly answer questions with high confidence. They further explore the calibration of language models, investigating the correlation between confidence and accuracy, as well as the consistency of responses when the same question is posed multiple times. The authors conclude that SimpleQA provides a valuable framework for evaluating the factuality of language models and encourages the development of more trustworthy and reliable models.
原文链接：https://openai.com/index/introducing-simpleqa/
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