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.

在这份调查中，我们旨在对大型语言模型（LLM）幻像领域的最新进展进行全面而深入的概述。我们从 LLM 幻像创新分类入手，然后深入探讨了导致幻像的因素。接下来，我们全面介绍了幻像检测方法和基准。此外，我们还相应介绍了用于减轻幻像的代表性方法。最后，我们分析了突出当前限制的挑战，并制定了未来 LLM 幻像研究的开放问题，旨在描绘发展方向。