Large Language Models (LLMs) demonstrate strong multilingual capabilities yet exhibit systematic cultural biases that affect entity-aware machine translation. While external knowledge integration improves translation accuracy, the extent of these benefits across varying degrees of cultural specificity remains unexplored. We propose a three-level cultural specificity framework: Culturally Agnostic, Culturally Sensitive, and Culturally Local, to systematically analyze how cultural context affects entity translation difficulty and the utility of external knowledge. Through experiments spanning 11 LLMs and 10 languages, we demonstrate that external knowledge provides substantially greater improvements for culturally local entities (up to 70% in m-ETA) compared to culturally agnostic ones. Our analysis reveals distinct behavioral patterns across model tiers: closed and open-weight models show synergistic improvements in both entity accuracy and overall translation quality, while open-data models struggle with instruction-following despite improved entity accuracy.

Data scarcity is a prevalent challenge in the era of Large Language Models (LLMs). The insatiable hunger of LLMs for large corpora becomes even more pronounced when dealing with non-English and low-resource languages. The issue is particularly exacerbated in Semantic Parsing (SP), i.e. the task of converting text into a formal representation. The complexity of semantic formalisms makes training human annotators and subsequent data annotation unfeasible on a large scale, especially across languages. To mitigate this, we first introduce the Multilingual Semantic Layer (MSL), a conceptual evolution of previous formalisms, which decouples from disambiguation and external inventories and simplifies the task. MSL provides the necessary tools to encode the meaning across languages, paving the way for developing a high-quality semantic parsing dataset across different languages in a semi-automatic strategy. Subsequently, we manually refine a portion of this dataset and fine-tune GPT-3.5 to propagate these refinements across the dataset. Then, we manually annotate 1,100 sentences in eleven languages, including low-resource ones. Finally, we assess our dataset’s quality, showcasing the performance gap reduction across languages in Semantic Parsing.