Detecting AI Hallucinations with Semantic Entropy

Detecting hallucinations in large language models using semantic entropy

Researchers have developed a novel method using semantic entropy to detect hallucinations—incorrect or arbitrary outputs—in large language models (LLMs). This method focuses on identifying "confabulations," where LLMs generate fluent but incorrect responses. By measuring uncertainty at the semantic level, rather than just looking at word sequences, this approach helps determine when an LLM's output is unreliable, enabling better oversight and more trustworthy AI applications, especially in critical fields like medical diagnostics and legal advice.