Detecting hallucinations in large language models using semantic entropy

This article likely discusses a research paper or a new technique for identifying when large language models (LLMs) generate incorrect or nonsensical information (hallucinations). Semantic entropy is probably used as a metric to quantify the uncertainty or randomness in the model's output, with higher entropy potentially indicating a hallucination. The source, Hacker News, suggests a technical audience and a focus on practical applications or advancements in AI.