  Natural Language Generation (NLG) has improved exponentially in recent years
thanks to the development of sequence-to-sequence deep learning technologies
such as Transformer-based language models. This advancement has led to more
fluent and coherent NLG, leading to improved development in downstream tasks
such as abstractive summarization, dialogue generation and data-to-text
generation. However, it is also apparent that deep learning based generation is
prone to hallucinate unintended text, which degrades the system performance and
fails to meet user expectations in many real-world scenarios. To address this
issue, many studies have been presented in measuring and mitigating
hallucinated texts, but these have never been reviewed in a comprehensive
manner before. In this survey, we thus provide a broad overview of the research
progress and challenges in the hallucination problem in NLG. The survey is
organized into two parts: (1) a general overview of metrics, mitigation
methods, and future directions; and (2) an overview of task-specific research
progress on hallucinations in the following downstream tasks, namely
abstractive summarization, dialogue generation, generative question answering,
data-to-text generation, machine translation, and visual-language generation.
This survey serves to facilitate collaborative efforts among researchers in
tackling the challenge of hallucinated texts in NLG.

Computation and Language