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。