The advent of instruction-tuned language models that convincingly mimic human
writing poses a significant risk of abuse. For example, such models could be
used for plagiarism, disinformation, spam, or phishing. However, such abuse may
be counteracted with the ability to detect whether a piece of text was composed
by a language model rather than a human. Some previous approaches to this
problem have relied on supervised methods trained on corpora of confirmed human
and machine-written documents. Unfortunately, model under-specification poses
an unavoidable challenge for neural network-based detectors, making them
brittle in the face of data shifts, such as the release of further language
models producing still more fluent text than the models used to train the
detectors. Other previous approaches require access to the models that may have
generated a document in question at inference or detection time, which is often
impractical. In light of these challenges, we pursue a fundamentally different
approach not relying on samples from language models of concern at training
time. Instead, we propose to leverage representations of writing style
estimated from human-authored text. Indeed, we find that features effective at
distinguishing among human authors are also effective at distinguishing human
from machine authors, including state of the art large language models like
Llama 2, ChatGPT, and GPT-4. Furthermore, given a handful of examples composed
by each of several specific language models of interest, our approach affords
the ability to predict which model generated a given document.

利用人类文本估计的写作风格来区分人类作者和机器作者，以及预测给定文档由哪个语言模型生成。