Impact of Intermediate-Task Selection on Few-Shot Cross-Lingual Transfer in Large Language Models

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 goal: How do different intermediate-task selection strategies affect the few-shot cross-lingual transfer performance of 1B and 10B parameter models on XTREME-R tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.