Language generation has been an important task in natural language processing (NLP) with increasing variety of applications especially in the recent years. The evaluation of generative language models typically rely on automatic heuristics which search for overlaps over word or phrase level patterns in generated outputs and traditionally some hand-crafted reference sentences in the given language ranging in the forms from sentences to entire documents. Language, on the other hand, is productive by nature, which means the same concept can be expressed potentially in many different lexical or phrasal forms, making the assessment of generated outputs a very difficult one. Many studies have indicated potential hazards related to the prominent choice of heuristics matching generated language to selected references and the limitations raised by this setting in developing robust generative models. This paper undertakes an in-depth analysis of evaluation metrics used for generative models, specifically investigating their responsiveness to various syntactic structures, and how these characteristics vary across languages with different morphosyntactic typologies. Preliminary findings indicate that while certain metrics exhibit robustness in particular linguistic contexts, a discernible variance emerges in their performance across distinct syntactic forms. Through this exploration, we highlight the imperative need for more nuanced and encompassing evaluation strategies in generative models, advocating for metrics that are sensitive to the multifaceted nature of languages.