Large language models are used for different tasks, including pinpointing financial fraud and translating articles. Despite their many capabilities, they may, at times, generate inaccurate results. In addition to this, models are sometimes underconfident about correct answers or overconfident even when their responses are wrong. This makes it hard for a user to know when to trust the answers generated by a model.

Normally, a model that’s well calibrated shouldn’t be very confident about an incorrect response, and vice versa. To ensure a machine-learning model’s confidence level matches its…

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