  As large language models continue to develop in the field of AI, text
generation systems are susceptible to a worrisome phenomenon known as
hallucination. In this study, we summarize recent compelling insights into
hallucinations in LLMs. We present a novel taxonomy of hallucinations from
various text generation tasks, thus provide theoretical insights, detection
methods and improvement approaches. Based on this, future research directions
are proposed. Our contribution are threefold: (1) We provide a detailed and
complete taxonomy for hallucinations appearing in text generation tasks; (2) We
provide theoretical analyses of hallucinations in LLMs and provide existing
detection and improvement methods; (3) We propose several research directions
that can be developed in the future. As hallucinations garner significant
attention from the community, we will maintain updates on relevant research
progress.
