Entropy-based Sentence-level Hallucination Score in Large Language Models

Large Language Models (LLMs) excel at generating human-like text but are prone to hallucinations-fabricated outputs that deviate from reality. Addressing this is critical for ensuring trustworthy AI, especially in fields like medicine, law, and education. This paper introduces an entropy-based metric for detecting sentence-level hallucinations by assessing token prediction uncertainty. High entropy correlates with hallucinatory outputs, and we validate this metric’s effectiveness across different LLMs. Applications include hallucination flagging and improving user trust through uncertainty-driven feedback. This approach advances the reliability and transparency of LLMs, contributing to safer AI systems.