Detecting when LLMs are Uncertain

XJDR's Entropix project introduces new reasoning techniques for language models (LLMs) that improve decision-making during moments of uncertainty through adaptive sampling. Entropix uses metrics such as entropy and varentropy to measure uncertainty and suggests different methods for choosing the next token based on these metrics. These techniques include branching predictions and inserting thinking tokens. The goal is to improve model performance and reasoning without significant computational overhead. Although no large-scale evaluations are available yet, Entropix presents promising tools for enhancing LLMs.