Zhichen Liu


2024

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Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood
Yang Xu | Yu Wang | Hao An | Zhichen Liu | Yongyuan Li
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

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.