<ul><li>Automated hallucination detection in large language models (LLMs) is analyzed in a theoretical framework.</li><li>The study establishes an equivalence between hallucination detection and language identification, concluding that detection is fundamentally impossible for most language collections if the detector is trained using only correct examples.</li><li>The use of expert-labeled feedback, including negative examples, makes automated hallucination detection possible for all countable language collections.</li><li>These findings support the importance of expert-labeled examples and feedback-based methods for reliable deployment of LLMs.</li></ul>

(Im)possibility of Automated Hallucination Detection in Large Language Models

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