  The emergence of large language models (LLMs) has marked a significant
breakthrough in natural language processing (NLP), leading to remarkable
advancements in text understanding and generation. Nevertheless, alongside
these strides, LLMs exhibit a critical tendency to produce hallucinations,
resulting in content that is inconsistent with real-world facts or user inputs.
This phenomenon poses substantial challenges to their practical deployment and
raises concerns over the reliability of LLMs in real-world scenarios, which
attracts increasing attention to detect and mitigate these hallucinations. In
this survey, we aim to provide a thorough and in-depth overview of recent
advances in the field of LLM hallucinations. We begin with an innovative
taxonomy of LLM hallucinations, then delve into the factors contributing to
hallucinations. Subsequently, we present a comprehensive overview of
hallucination detection methods and benchmarks. Additionally, representative
approaches designed to mitigate hallucinations are introduced accordingly.
Finally, we analyze the challenges that highlight the current limitations and
formulate open questions, aiming to delineate pathways for future research on
hallucinations in LLMs.
