Typological Distance and Zero-Shot Cross-Lingual Transfer in XTREME-R

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-li

Research goal: How does the typological distance between the intermediate-task training language and the target language correlate with zero-shot cross-lingual transfer accuracy in XTREME-R benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.