Since language models produce fake text quickly and easily, there is an oversupply of such content in the public domain. The degree of sophistication and writing style has reached a point where differentiating between human authored and machine-generated content is nearly impossible. As a result, works generated by language models rather than human authors have gained significant media attention and stirred controversy.Concerns regarding the possible influence of advanced language models on society have also arisen, needing a fuller knowledge of these processes. Natural language generation (NLG) and generative pre-trained transformer (GPT) models have revolutionized a variety of sectors: the scope not only permeated throughout journalism and customer service but also reached academia. To mitigate the hazardous implications that may arise from the use of these models, preventative measures must be implemented, such as providing human agents with the capacity to distinguish between artificially made and human composed texts utilizing automated systems and possibly reverse-engineered language models. Furthermore, to ensure a balanced and responsible approach, it is critical to have a full grasp of the socio-technological ramifications of these breakthroughs. This literature survey aims to compile and synthesize accomplishments and developments in the aforementioned work, while also identifying future prospects. It also gives an overview of machine-generated text trends and explores the larger societal implications. Ultimately, this survey intends to contribute to the development of robust and effective approaches for resolving the issues connected with the usage and detection of machine-generated text by exploring the interplay between the capabilities of language models and their possible implications.
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Max Planck Institute（马克斯·普朗克研究所）；ETH Zürich（苏黎世联邦理工学院）；UC San Diego（加州大学圣迭戈分校）；University of Waterloo（滑铁卢大学）；University of Michigan（密歇根大学）
University of Notre Dame（圣母大学）；Microsoft Research（微软研究院）；The Chinese University of Hong Kong（香港中文大学）；University of California at San Diego（加州大学圣迭戈分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
A survey on text generation using generative adversarial networks
São Paulo State University（圣保罗州立大学）
本文对使用生成对抗网络进行文本生成的研究进展进行了全面的综述。使用对抗学习来生成文本是有希望的，因为它提供了生成所谓的“自然”语言的替代方案。然而，对抗性文本生成并不是一项简单的任务，因为其最重要的架构生成对抗网络被设计用于处理连续信息（图像）而不是离散数据（文本）。因此，大多数作品都基于三种可能的选择，即，Gumbel-Softmax微分，强化学习和修改的训练目标。所有的替代品在本次调查中进行审查，因为他们提出了最新的方法来生成文本使用基于对抗的技术。所选的作品来自著名的数据库，如Science Direct，IEEEEXplore，Springer，Association for Computing Machinery和arXiv，而每一个选定的工作都经过严格的分析和评估，以呈现其目标，方法和实验结果。
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；University of Cambridge（剑桥大学）；The Chinese University of Hong Kong（香港中文大学）；Tencent AI Lab（腾讯人工智能实验室）
Diffusion Models for Non-autoregressive Text Generation: A Survey
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Information, Renmin University of China（中国人民大学信息学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京大数据管理与分析方法重点实验室）
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
Michigan State University（密歇根州立大学）；Virginia Tech（弗吉尼亚理工大学）；Sun Yat-sen University（中山大学）；Zhejiang University of Technology（浙江工业大学）；Tencent Jarvis Lab（腾讯Jarvis实验室）；University of Sheffield（谢菲尔德大学）；Université de Montréal（蒙特利尔大学）；Mila（Mila蒙特利尔学习算法研究所）
SciFive: a text-to-text transformer model for biomedical literature
Case Western University（凯斯西储大学）；National Cancer Institute（国家癌症研究所）；Vietnam National University（越南国家大学）；University of Science, Vietnam National University（越南国家大学科学大学）
Deep Literature Survey Automation with an Iterative Workflow
Zhejiang University（浙江大学）；School of Engineering, Westlake University（西湖大学工程学院）；Peking University（北京大学）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖先进研究院技术研究所）
A Survey of Text Watermarking in the Era of Large Language Models
Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing University of Posts and Telecommunications（北京邮电大学）；University of Illinois Chicago（芝加哥大学伊利诺伊分校）
Quasi Error-free Text Classification and Authorship Recognition in a large Corpus of English Literature based on a Novel Feature Set
Freie Universität Berlin（柏林自由大学）；Center for Cognitive Neuroscience (CCNB), Freie Universität Berlin（柏林自由大学认知神经科学中心）；Learning Psychology, Freie Universität Berlin（柏林自由大学学习心理学系）
Recent Advances in Hierarchical Multi-label Text Classification: A Survey
Beijing University Of Posts and Telecommunications（北京邮电大学）；Patent Examination Cooperation, CNIPA（国家知识产权局专利审查协作中心）；Zhongtukexin Co. Ltd.（中图科信有限公司）；Deakin University（迪肯大学）
