Entropy-based sampling is a technique based on the theory of entropy, aimed at enhancing the diversity and accuracy of language model outputs when generating text. It evaluates model uncertainty by calculating the entropy and variance entropy of the probability distribution, allowing for adjustments in sampling strategy when the model may become trapped in local optima or overly confident. This method helps avoid monotonous repetition in outputs while increasing diversity during periods of high model uncertainty.