Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood

• URL: http://arxiv.org/abs/2406.19874v2
• Date: Wed, 09 Oct 2024 09:36:49 GMT
• Title: Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood
• Authors: Yang Xu, Yu Wang, Hao An, Zhichen Liu, Yongyuan Li,
• Abstract summary: This study provides a new perspective by using the relative likelihood values instead of absolute ones. We propose a detection procedure with two classification methods, supervised and supervised-based, respectively. Our method can also reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies.
• Score: 5.404146472517001
• License:
• Abstract: Human and model-generated texts can be distinguished by examining the magnitude of likelihood in language. However, it is becoming increasingly difficult as language model's capabilities of generating human-like texts keep evolving. This study provides a new perspective by using the relative likelihood values instead of absolute ones, and extracting useful features from the spectrum-view of likelihood for the human-model text detection task. We propose a detection procedure with two classification methods, supervised and heuristic-based, respectively, which results in competitive performances with previous zero-shot detection methods and a new state-of-the-art on short-text detection. Our method can also reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies. Our code is available at https://github.com/CLCS-SUSTech/FourierGPT

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