Abstract

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, and machine translation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

Keywords

HallucinatingNatural language generationAutomatic summarizationComputer scienceGenerative grammarNaturalnessMachine translationArtificial intelligenceNatural language processingDeep learningTypologyGrammaticalityNatural languageLinguistics

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Publication Info

Year
2022
Type
review
Volume
55
Issue
12
Pages
1-38
Citations
2392
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

2392
OpenAlex
120
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Cite This

Ziwei Ji, Nayeon Lee, Rita Frieske et al. (2022). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys , 55 (12) , 1-38. https://doi.org/10.1145/3571730

Identifiers

DOI
10.1145/3571730
arXiv
2202.03629

Data Quality

Data completeness: 84%