Semantic Entropy

 A measure of uncertainty or unpredictability in the meaning of a piece of information. Unlike traditional entropy, which quantifies uncertainty in a set of data, semantic entropy focuses on the variability and ambiguity in interpreting the meaning and context behind the data. High semantic entropy indicates greater complexity and difficulty in pinpointing a definitive interpretation, which can lead to issues such as hallucinations in AI outputs. Reducing semantic entropy involves enhancing the contextual understanding and accuracy of AI models, leading to more reliable and coherent information processing.