Measuring model calibration using response frequency
Assess model calibration by checking if answer frequency correlates with accuracy over multiple attempts.
Ask the language model the same question multiple times (e.g., 100). Group similar answers and measure the accuracy of answers based on how frequently they appear. Higher frequency should ideally correlate with higher accuracy.
Plot accuracy against response frequency. A well-calibrated model's accuracy should match its response frequency. This helps understand if frequent answers are indeed correct.
Ask a model the same question 100 times. Analyze the plot showing accuracy increases with response frequency, noting if the frequency roughly equals accuracy for well-calibrated models.
Evaluate language model calibration by repeatedly asking the same question and observing answer frequency. If frequent answers are more accurate, the model shows better calibration, indicating it's more confident in correct responses.
This method assumes that a model is more likely to consistently produce a correct answer if it truly 'knows' it. The frequency of a specific answer serves as a proxy for the model's confidence in that answer.
When the model is designed to be non-deterministic or when assessing single-shot performance.
- Introducing SimpleQA ↗OpenAI · OpenAI News · Oct 30, 2024 · paraphrased
First seen on Subvolts Oct 30, 2024. Updated Sep 11, 2026. Spotted a problem? Tell us.
- Introducing SimpleQAOpenAI · OpenAI News · Oct 30, 2024
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
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- OpenAI (OpenAI News). Subvolts collected, normalized and credited it; the link above opens the original.