Detecting subtle differences between human and model languages using spectrum of relative likelihood Xu, Yang Wang, Yu An, Hao Liu, Zhichen Li, Yongyuan 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. ACL 2024 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/61177 Xu Y, Wang Y, An H, Liu Z, Li Y. Detecting subtle differences between human and model languages using spectrum of relative likelihood. In: <i>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</i>. ACL; 2024:10108–10121. doi:<a href="https://doi.org/10.18653/v1/2024.emnlp-main.564">10.18653/v1/2024.emnlp-main.564</a> eng info:eu-repo/semantics/altIdentifier/doi/10.18653/v1/2024.emnlp-main.564 info:eu-repo/semantics/openAccess