The Digital Press

This work explores whether it is possible to automatically detect hallucinations—false or incorrect outputs—produced by large language models (LLMs). It introduces a theoretical framework linking hallucination detection to the classical problem of language identification.

Why it matters: Automatic hallucination detection is crucial for trustworthy LLM use but is fundamentally hard without expert feedback.

The big picture: Detecting hallucinations is equivalent to identifying languages, a notoriously difficult problem, if only correct examples are used.

Stunning stat: Hallucination detection is impossible for most language sets when trained solely on correct statements without negative examples.

Quick takeaway: Incorporating expert-labeled incorrect examples enables reliable hallucination detection across all countable language collections.