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arXiv AI By Naveen Lamba, Sanju Tiwari, Manas Gaur

Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention

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The paper surveys hallucinations in large language models (LLMs) through a lifecycle lens, covering causes, detection, mitigation, and prevention. It categorizes hallucinations into data‑related, training‑related, and inference‑related stages, aligning each with specific interventions. The authors also review benchmark datasets and propose a standardized framework to diagnose and address hallucinations for safer, more reliable LLMs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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