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.

翻译：近年来,由于开发了深层学习技术,如以变异器为基础的语言模型等,自然语言生成有了显著的改善,这种进步导致更流畅和一致的自然语言生成,自然导致下游任务的发展,如抽象总结、对话生成和数据到文字生成,然而,人们也调查,这种生成包括了幻觉文本,使文本生成的性能在许多现实世界情景中无法满足用户的期望。为了解决这一问题,在各种任务中提出了评估和减轻幻觉的方法,但并未以综合方式加以审查。在本调查中,我们广泛概述了NLG幻觉问题的研究进展和挑战。调查分为两大部分:(一) 衡量、缓解方法和今后方向的总体概览;(二) 一系列大型下游任务中幻觉的具体任务研究进展:抽象总结、对话生成、归异性解答、数据到文字生成和机器翻译。这一调查可以促进研究人员在这些任务中的合作努力。