Multiscale Positive-Unlabeled Detection of AI-Generated Texts

• URL: http://arxiv.org/abs/2305.18149v4
• Date: Tue, 5 Mar 2024 08:27:12 GMT
• Title: Multiscale Positive-Unlabeled Detection of AI-Generated Texts
• Authors: Yuchuan Tian, Hanting Chen, Xutao Wang, Zheyuan Bai, Qinghua Zhang, Ruifeng Li, Chao Xu, Yunhe Wang
• Abstract summary: Multiscale Positive-Unlabeled (MPU) training framework is proposed to address the difficulty of short-text detection. MPU method augments detection performance on long AI-generated texts, and significantly improves short-text detection of language model detectors.
• Score: 27.956604193427772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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