Applicability of multilingual language models across different settings are conventionally evaluated using task and language-specific performance in downstream benchmarks, which are often lacking in many languages. Our recent study explores a set of efficient and reliable measures that could aid in computing more information related to the generalization capability of language models in cross-lingual zero-shot settings. Preprint available on the arxiv.