The Uniform Information Density principle posits that humans prefer to spread
information evenly during language production. In this work, we examine if the
UID principle can help capture differences between Large Language Models (LLMs)
and human-generated text. We propose GPT-who, the first
psycholinguistically-aware multi-class domain-agnostic statistical-based
detector. This detector employs UID-based features to model the unique
statistical signature of each LLM and human author for accurate authorship
attribution. We evaluate our method using 4 large-scale benchmark datasets and
find that GPT-who outperforms state-of-the-art detectors (both statistical- &
non-statistical-based) such as GLTR, GPTZero, OpenAI detector, and ZeroGPT by
over $20$% across domains. In addition to superior performance, it is
computationally inexpensive and utilizes an interpretable representation of
text articles. We present the largest analysis of the UID-based representations
of human and machine-generated texts (over 400k articles) to demonstrate how
authors distribute information differently, and in ways that enable their
detection using an off-the-shelf LM without any fine-tuning. We find that
GPT-who can distinguish texts generated by very sophisticated LLMs, even when
the overlying text is indiscernible.

通过 UID 原则构建了 GPT-who 检测器，它利用 UID 特征对每个大型语言模型和人类作者的统计签名进行建模，以实现准确的作者归属。GPT-who 在四个大规模基准数据集上表现优于现有的检测器，并能够区分由非常复杂的大型语言模型生成的文本，即使上层文本无法辨别。