Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909341611720704 |
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| author | Xu, Yang Wang, Yu An, Hao Liu, Zhichen Li, Yongyuan |
| author_facet | Xu, Yang Wang, Yu An, Hao Liu, Zhichen Li, Yongyuan |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19874 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood Xu, Yang Wang, Yu An, Hao Liu, Zhichen Li, Yongyuan Computation and Language Artificial Intelligence I.2.7 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 |
| title | Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood |
| topic | Computation and Language Artificial Intelligence I.2.7 |
| url | https://arxiv.org/abs/2406.19874 |