  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
