Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore

• URL: http://arxiv.org/abs/2405.04286v1
• Date: Tue, 7 May 2024 12:57:01 GMT
• Title: Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore
• Authors: Junchao Wu, Runzhe Zhan, Derek F. Wong, Shu Yang, Xuebo Liu, Lidia S. Chao, Min Zhang,
• Abstract summary: We propose a simple but effective black-box zero-shot detection approach. It is predicated on the observation that human-written texts typically contain more grammatical errors than LLM-generated texts. Our method achieves an average AUROC of 98.7% and shows strong robustness against paraphrase and adversarial perturbation attacks.
• Score: 51.65730053591696
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The efficacy of an large language model (LLM) generated text detector depends substantially on the availability of sizable training data. White-box zero-shot detectors, which require no such data, are nonetheless limited by the accessibility of the source model of the LLM-generated text. In this paper, we propose an simple but effective black-box zero-shot detection approach, predicated on the observation that human-written texts typically contain more grammatical errors than LLM-generated texts. This approach entails computing the Grammar Error Correction Score (GECScore) for the given text to distinguish between human-written and LLM-generated text. Extensive experimental results show that our method outperforms current state-of-the-art (SOTA) zero-shot and supervised methods, achieving an average AUROC of 98.7% and showing strong robustness against paraphrase and adversarial perturbation attacks.

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