This paper explains that hallucinations in large language models (LLMs) aren’t due to flawed data, but to the way these models are trained and evaluated. LLMs are incentivized to guess rather than admit uncertainty, leading to errors that are statistically predictable. The authors frame this as a binary classification problem – correctly identifying valid outputs – and demonstrate a link between misclassification rate and hallucination rate. They argue that fixing this requires a shift in evaluation metrics, moving away from rewarding overconfidence and towards accepting uncertainty, to build more trustworthy models.
Entropy, once seen as a measure of disorder in physical systems, is now understood as a reflection of our ignorance and knowledge limitations. This evolving perspective links entropy to information theory and challenges traditional views of objectivity in science.
A hands-on tutorial in Python for sensor engineers on Bayesian sensor calibration, which combines statistical models and data to optimally calibrate sensors. This technique is crucial in engineering to minimize sensor measurement uncertainty. The tutorial provides Python code to perform such calibration numerically using existing libraries.
