DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions

• URL: http://arxiv.org/abs/2310.14479v1
• Date: Mon, 23 Oct 2023 01:23:10 GMT
• Title: DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions
• Authors: Rongsheng Wang, Qi Li, Sihong Xie
• Abstract summary: Existing detectors are built on the assumption that there is a distribution gap between human-generated and AI-generated texts. We find that large language models such as ChatGPT exhibit strong self-consistency in text generation and continuation. We propose a new method for AI-generated texts detection based on self-consistency with masked predictions.
• Score: 13.077729125193434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: General large language models (LLMs) such as ChatGPT have shown remarkable success, but it has also raised concerns among people about the misuse of AI-generated texts. Therefore, an important question is how to detect whether the texts are generated by ChatGPT or by humans. Existing detectors are built on the assumption that there is a distribution gap between human-generated and AI-generated texts. These gaps are typically identified using statistical information or classifiers. In contrast to prior research methods, we find that large language models such as ChatGPT exhibit strong self-consistency in text generation and continuation. Self-consistency capitalizes on the intuition that AI-generated texts can still be reasoned with by large language models using the same logical reasoning when portions of the texts are masked, which differs from human-generated texts. Using this observation, we subsequently proposed a new method for AI-generated texts detection based on self-consistency with masked predictions to determine whether a text is generated by LLMs. This method, which we call DetectGPT-SC. We conducted a series of experiments to evaluate the performance of DetectGPT-SC. In these experiments, we employed various mask scheme, zero-shot, and simple prompt for completing masked texts and self-consistency predictions. The results indicate that DetectGPT-SC outperforms the current state-of-the-art across different tasks.

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