  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.
