\subsubsection{Models Performances Across Local Contexts}
We analyzed the presence of cultural variation in LLM performances through an analysis their results, aggregated by macro-regions. In order to avoid cases in which one language was associated to a single macro-region, we jointly considered North and West Europe regions (English and German) and East and South East Asia regions (Burmese, Chinese, and Khmer). We only considered LLMs zero-shot performances, to observe models behavior without seeing examples from the dataset. The average F-score across the seven benchmarked LLMs are reported in Table \ref{tab:region-scores}. As it is possible to observe, European regions obtain the highest results, ranging from an F-score of $0.681$ and $0.701$. Non-European regions get significantly lower scores, ranging from an F-score of $0.604$ to an F-score of $0.424$. Interestingly, there are specific patterns emerge between regions within the same continents: languages from Eastern-Africa and South-Eastern Asia always obtain a lower F-score than African and Asian countries from different regions. This might suggest that language and cultural biases intersect in determining this high variation in performances.                

\begin{table}[t]
\centering
\begin{tabular}{lc}
\toprule
\textbf{Region} & \textbf{Score} \\
\midrule
S-Europe   & 0.701 \\
N- \& W-Europe  & 0.694 \\
E-Europe   & 0.681 \\
W-Asia     & 0.604 \\
S-Asia     & 0.575 \\
W-Africa   & 0.536 \\
E- \& SE-Asia    & 0.450 \\
E-Africa   & 0.424 \\
\bottomrule
\end{tabular}
\caption{Scores by geographic region.}
\label{tab:region-scores}
\end{table}
