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.
翻译:近年来,自然语言生成(NLG)由于开发了诸如以变异器为基础的语言模型等测序到测序的深层次学习技术,近年来有了成倍的改善,自然语言生成(NLG)近年来有了成倍的改善;这一进展使NLG更加流畅和连贯,从而改进了下游任务的发展,例如抽象总结、对话生成和数据到文字生成;然而,同样明显的是,深层次的基于学习的生成容易产生幻觉的意外文字,从而降低系统性能,在许多现实世界情景中不能满足用户的期望;为解决这一问题,在测量和减轻幻觉文本方面提出了许多研究报告,但以前从未以全面的方式加以审查;在本次调查中,我们因此对NLG幻觉问题的研究进展和挑战进行了广泛的概述。调查分为两个部分:(1) 衡量标准、减缓方法和未来方向的总体概览;以及(2) 概述下游任务中幻觉方面的具体研究进展,即抽象总结、对话生成、基因化问题解答、数据到文字生成、机器翻译和视觉语言生成。