Significant progress has been made on text generation by pre-trained language
models (PLMs), yet distinguishing between human and machine-generated text
poses an escalating challenge. This paper offers an in-depth evaluation of
three distinct methods used to address this task: traditional shallow learning,
Language Model (LM) fine-tuning, and Multilingual Model fine-tuning. These
approaches are rigorously tested on a wide range of machine-generated texts,
providing a benchmark of their competence in distinguishing between
human-authored and machine-authored linguistic constructs. The results reveal
considerable differences in performance across methods, thus emphasizing the
continued need for advancement in this crucial area of NLP. This study offers
valuable insights and paves the way for future research aimed at creating
robust and highly discriminative models.

通过对三种不同方法进行深入评估，即传统的浅层学习、语言模型（LM）微调和多语言模型微调，本文对文本生成的重要性有了显著进展，尤其是在区分人类和机器生成文本方面，结果表明这些方法在性能上存在显著差异，强调了在自然语言处理领域中继续推进的重要性，为未来创造强大且具有高度辨别力的模型铺平了道路。

突破图灵：检测机器生成文本方法的比较分析

Beyond Turing: A Comparative Analysis of Approaches for Detecting  Machine-Generated Text

While recent advancements in the capabilities and widespread accessibility of
generative language models, such as ChatGPT (OpenAI, 2022), have brought about
various benefits by generating fluent human-like text, the task of
distinguishing between human- and large language model (LLM) generated text has
emerged as a crucial problem. These models can potentially deceive by
generating artificial text that appears to be human-generated. This issue is
particularly significant in domains such as law, education, and science, where
ensuring the integrity of text is of the utmost importance. This survey
provides an overview of the current approaches employed to differentiate
between texts generated by humans and ChatGPT. We present an account of the
different datasets constructed for detecting ChatGPT-generated text, the
various methods utilized, what qualitative analyses into the characteristics of
human versus ChatGPT-generated text have been performed, and finally, summarize
our findings into general insights

用于区分 ChatGPT 生成文本和人类生成文本的当前方法的综述，包括构建用于检测 ChatGPT 生成文本的不同数据集，采用的各种方法，对人类与 ChatGPT 生成文本特征的定性分析，并最终总结研究结果为一般性见解。