Detecting hallucinations in large language models using semantic entropy
Abstract
Large language models (LLMs) have revolutionized natural language processing, but their tendency to "hallucinate"—generating factually incorrect or nonsensical information—remains a significant challenge. Current methods for detecting hallucinations often rely on external knowledge bases or human annotation, which can be resource-intensive. This paper introduces a novel approach for hallucination detection in LLMs based on semantic entropy. Our method quantifies the uncertainty and inconsistency in an LLM's generated output by analyzing the semantic diversity of multiple plausible continuations. A higher semantic entropy indicates a greater likelihood of hallucination, as the model's confidence in a single, coherent factual statement is diminished. This zero-shot detection mechanism provides a robust and efficient way to identify hallucinations without requiring external factual supervision.