  A model's capacity to generalize its knowledge to interpret unseen inputs
with different characteristics is crucial to build robust and reliable machine
learning systems. Language model evaluation tasks lack information metrics
about model generalization and their applicability in a new setting is measured
using task and language-specific downstream performance, which is often lacking
in many languages and tasks. In this paper, we explore 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. In addition to traditional measures such as variance in parameters
after training and distance from initialization, we also measure the
effectiveness of sharpness in loss landscape in capturing the success in
cross-lingual transfer and propose a novel and stable algorithm to reliably
compute the sharpness of a model optimum that correlates to generalization.
