Recently, large language models (LLMs) have demonstrated unprecedented capabilities in language generation, yet they still often produce incorrect information. Therefore, determining whether a text was generated by an LLM has become one of the factors that must be considered when evaluating its reliability. In this paper, we discuss methods to determine whether texts written in various languages were authored by humans or generated by LLMs. We have discovered that the classification accuracy significantly decreases for texts written in languages not observed during the training process, and we aim to address this issue. We propose a method to improve performance for unseen languages by using token-level predictive distributions extracted from various LLMs and text embeddings from a multilingual pre-trained langauge model. With the proposed method, we achieved third place out of 25 teams in Subtask B (binary multilingual machine-generated text detection) of Shared Task 1, with an F1 macro score of 0.7532.