What large language models know and what people think they know - Nature Machine Intelligence
doi.org
Understanding how people perceive and interpret uncertainty from large language models (LLMs) is crucial, as users often overestimate LLM accuracy, especially with default explanations. Steyvers et al. show that aligning LLM explanations with their internal confidence improves user perception.
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The original was posted on /r/science by /u/calliope_kekule on 2025-01-28 05:57:15+00:00.

Original Title: A new study explores how human confidence in large language models (LLMs) often surpasses their actual accuracy. It highlights the ‘calibration gap’ - the difference between what LLMs know and what users think they know.