The ability to detect refactoring is crucial to understanding software evolution. Many studies have proposed automated refactoring detection tools, such as RefactoringMiner and PyRef, but most are language-specific. Developing tools for new languages requires creating custom rules for each language, which is time-consuming and labor-intensive. This paper empirically evaluates the potential of Cross-Language Transfer (CLT) Learning using Large Language Models (LLMs) as a more general and cost-effective alternative for refactoring detection. Specifically, we compare CLT prompting against zero-shot prompting and native few-shot prompting. In CLT prompting, examples from one language (Java) are used to detect refactorings in another language (Python). In native few-shot prompting, examples are written in the same language as the target. Our results show that cross-language (Java→Python) transfer is highly effective, significantly outperforming the zero-shot prompting (Avg. F1 0.501 vs. 0.430). Surprisingly, it nearly matches the performance of native few-shot prompting (Python→Python, Avg. F1 0.506). These findings suggest that CLT is a promising direction for bypassing language-specific tool development.