With the increasing use of Artificial Intelligence in Natural Language
Processing, concerns have been raised regarding the detection of AI-generated
text in various domains. This study aims to investigate this issue by proposing
a methodology to accurately distinguish AI-generated and human-written book
reviews. Our approach utilizes transfer learning, enabling the model to
identify generated text across different topics while improving its ability to
detect variations in writing style and vocabulary. To evaluate the
effectiveness of the proposed methodology, we developed a dataset consisting of
real book reviews and AI-generated reviews using the recently proposed Vicuna
open-source language model. The experimental results demonstrate that it is
feasible to detect the original source of text, achieving an accuracy rate of
96.86%. Our efforts are oriented toward the exploration of the capabilities and
limitations of Large Language Models in the context of text identification.
Expanding our knowledge in these aspects will be valuable for effectively
navigating similar models in the future and ensuring the integrity and
authenticity of human-generated content.

本研究提出了一种方法来准确区分 AI 生成和人工撰写的书评，通过使用迁移学习，该方法能够在不同主题上识别生成的文本，并提高对写作风格和词汇变化的检测能力。实验结果表明，能够检测文本的原始来源，达到了 96.86% 的准确率。为了确保人工生成内容的完整性和真实性，扩大对大型语言模型在文本识别方面的能力和限制的理解对于有效地应用类似的模型以及确保内容的整体和真实性将是有价值的。

谁撰写评论，人类还是人工智能？

Who Writes the Review, Human or AI?

Watermarking approaches are proposed to identify if text being circulated is
human or large language model (LLM) generated. The state-of-the-art
watermarking strategy of Kirchenbauer et al. (2023a) biases the LLM to generate
specific (``green'') tokens. However, determining the robustness of this
watermarking method is an open problem. Existing attack methods fail to evade
detection for longer text segments. We overcome this limitation, and propose
{\em Self Color Testing-based Substitution (SCTS)}, the first ``color-aware''
attack. SCTS obtains color information by strategically prompting the
watermarked LLM and comparing output tokens frequencies. It uses this
information to determine token colors, and substitutes green tokens with
non-green ones. In our experiments, SCTS successfully evades watermark
detection using fewer number of edits than related work. Additionally, we show
both theoretically and empirically that SCTS can remove the watermark for
arbitrarily long watermarked text.

水印技术的方法可以用来识别是否为人类生成的文本还是大型语言模型（LLM）生成的文本。我们提出了一种第一种 “颜色感知” 攻击方法，可以成功地逃避水印的检测，并且能够移除任意长的水印文本。