Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent natural language generation, naturally leading to development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also investigated that such generation includes hallucinated texts, which makes the performances of text generation fail to meet users' expectations in many real-world scenarios. In order to address this issue, studies in evaluation and mitigation methods of hallucinations have been presented in various tasks, but have not been reviewed in a combined manner. In this survey, we provide a broad overview of the research progress and challenges in the hallucination problem of NLG. The survey is organized into two big divisions: (i) a general overview of metrics, mitigation methods, and future directions; (ii) task-specific research progress for hallucinations in a large set of downstream tasks: abstractive summarization, dialogue generation, generative question answering, data-to-text generation, and machine translation. This survey could facilitate collaborative efforts among researchers in these tasks.

本文综述了关于深度学习在自然语言生成中出现幻觉问题的度量、缓解方法和未来方向，以及针对抽象摘要、对话生成、生成式问答、数据到文本生成、机器翻译和视觉语言生成的幻觉问题的任务特定研究进展。

自然语言生成中幻觉的调查