Robustness in Multilingual Models: Intermediate-Task Training Duration and Typological Differences

Abstract

Abstract: Despite remarkable advancements in few-shot generalization in natural language processing, most models are developed and evaluated primarily in English. To facilitate research on few-shot cross-lingual transfer, we introduce a new benchmark, called BUFFET, which unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format and provides a fixed set of few-shot examples and instructions. BUFFET is designed to establish a rigorous and equitable evaluation framework for few-shot cross-lingual transfer across a broad range of tasks and languages. Using BUFFET, we perform thorough ev

Research Question

What is the impact of intermediate-task training duration on the robustness of multilingual models against typological differences in zero-shot cross-lingual transfer?

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