  While large language models (LLMs) have demonstrated remarkable capabilities
across a range of downstream tasks, a significant concern revolves around their
propensity to exhibit hallucinations: LLMs occasionally generate content that
diverges from the user input, contradicts previously generated context, or
misaligns with established world knowledge. This phenomenon poses a substantial
challenge to the reliability of LLMs in real-world scenarios. In this paper, we
survey recent efforts on the detection, explanation, and mitigation of
hallucination, with an emphasis on the unique challenges posed by LLMs. We
present taxonomies of the LLM hallucination phenomena and evaluation
benchmarks, analyze existing approaches aiming at mitigating LLM hallucination,
and discuss potential directions for future research.
