Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks, yet their evaluation, particularly in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations. Employing LLMs as evaluators to rank or score other models’ outputs emerges as a viable solution, addressing the constraints tied to human annotators and established benchmarks. In this study, we explore the potential of LLM-based evaluators in enhancing multilingual evaluation by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages. Our analysis reveals a bias in LLM-based evaluators towards higher scores, underscoring the necessity of calibration with native speaker judgments, especially in low-resource and non-Latin script languages, to ensure accurate evaluation of LLM performance across diverse languages.