BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual
  Transfer
Akari Asai Sneha Kudugunta Xinyan Velocity Yu Terra Blevins Hila Gonen Machel Reid Yulia Tsvetkov Sebastian Ruder Hannaneh Hajishirzi
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
evaluations of state-of-the-art multilingual large language models with
different transfer methods, namely in-context learning and fine-tuning. Our
findings reveal significant room for improvement in few-shot in-context
cross-lingual transfer. In particular, ChatGPT with in-context learning often
performs worse than much smaller mT5-base models fine-tuned on English task
data and few-shot in-language examples. Our analysis suggests various avenues
for future research in few-shot cross-lingual transfer, such as improved
pretraining, understanding, and future evaluations.