The burgeoning progress in the field of Large Language Models (LLMs) heralds
significant benefits due to their unparalleled capacities. However, it is
critical to acknowledge the potential misuse of these models, which could give
rise to a spectrum of social and ethical dilemmas. Despite numerous preceding
efforts centered around distinguishing synthetic text, most existing detection
systems fail to identify data synthesized by the latest LLMs, such as ChatGPT
and GPT-4. In response to this challenge, we introduce an unpretentious yet
potent detection approach proficient in identifying synthetic text across a
wide array of fields. Moreover, our detector demonstrates outstanding
performance uniformly across various model architectures and decoding
strategies. It also possesses the capability to identify text generated
utilizing a potent detection-evasion technique. Our comprehensive research
underlines our commitment to boosting the robustness and efficiency of
machine-generated text detection mechanisms, particularly in the context of
swiftly progressing and increasingly adaptive AI technologies.

本篇研究针对近年来大模型语言技术的快速普及及不断提高的适应性，提出了具有高效性和稳定性的人造文本检测方法。该方法能够辨别多种模型和解码策略生成的人造文本，并具有抗检测的能力。此外，研究还对机器生成文本检测机制的鲁棒性做出了贡献，揭示了在使用大规模语言模型技术时存在的社会和伦理问题，并提出了相应的解决方案。

G3Detector：通用 GPT 生成文本检测器

G3Detector: General GPT-Generated Text Detector

Within the text analysis and processing fields, generated text attacks have
been made easier to create than ever before. To combat these attacks open
sourcing models and datasets have become a major trend to create automated
detection algorithms in defense of authenticity. For this purpose, synthetic
text detection has become an increasingly viable topic of research. This review
is written for the purpose of creating a snapshot of the state of current
literature and easing the barrier to entry for future authors. Towards that
goal, we identified few research trends and challenges in this field.

本文综述了目前文献的研究趋势和挑战，旨在为今后的作者降低进入门槛，通过开源模型和数据集来帮助应对恶意生成文本攻击和创建自动检测算法的问题，特别是对合成文本检测领域的研究进行了概述。