Taming the AI Imagination: Quantifying Uncertainty in Language Models

A new approach leverages the principles of quantum mechanics to better understand and mitigate the tendency of large language models to generate factually incorrect or nonsensical text.
![The discretization of Burgers’ problem using a second-order MUSCL scheme, when integrated with a fourth-order Runge-Kutta method ([latex]RK44[/latex]), demonstrates that [latex]RK44[/latex] preserves the Total Variation Diminishing (TVD) property initially established by a forward Euler solution, even with variable time steps ([latex]\Delta t = \Delta t_{FE}[/latex]).](https://arxiv.org/html/2601.18947v1/figures/MUSCL2_TVD_RK4.png)


