  Recent releases of Large Language Models (LLMs), e.g. ChatGPT, are
astonishing at generating human-like texts, but they may impact the
authenticity of texts. Previous works proposed methods to detect these
AI-generated texts, including simple ML classifiers, pretrained-model-based
zero-shot methods, and finetuned language classification models. However,
mainstream detectors always fail on short texts, like SMSes, Tweets, and
reviews. In this paper, a Multiscale Positive-Unlabeled (MPU) training
framework is proposed to address the difficulty of short-text detection without
sacrificing long-texts. Firstly, we acknowledge the human-resemblance property
of short machine texts, and rephrase AI text detection as a partial
Positive-Unlabeled (PU) problem by regarding these short machine texts as
partially ``unlabeled". Then in this PU context, we propose the
length-sensitive Multiscale PU Loss, where a recurrent model in abstraction is
used to estimate positive priors of scale-variant corpora. Additionally, we
introduce a Text Multiscaling module to enrich training corpora. Experiments
show that our MPU method augments detection performance on long AI-generated
texts, and significantly improves short-text detection of language model
detectors. Language Models trained with MPU could outcompete existing detectors
on various short-text and long-text detection benchmarks. The codes are
available at
https://github.com/mindspore-lab/mindone/tree/master/examples/detect_chatgpt
and https://github.com/YuchuanTian/AIGC_text_detector.
