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.

翻译：随着大语言模型（LLMs）领域的蓬勃发展，其卓越能力带来了显著效益。然而，必须认识到这些模型可能被滥用的风险，这将引发一系列社会与伦理困境。尽管此前已有众多研究致力于区分合成文本，但现有检测系统大多无法识别由最新LLMs（如ChatGPT和GPT-4）生成的数据。针对这一挑战，我们提出了一种简洁而高效的检测方法，能够跨广泛领域识别合成文本。此外，我们的检测器在不同模型架构和解码策略下均表现出卓越的一致性性能，并具备识别采用强力规避检测技术生成的文本的能力。本研究的全面开展彰显了我们在提升机器生成文本检测机制的鲁棒性与效率方面的努力，尤其是在人工智能技术飞速发展且适应性日益增强的背景下。