Published July 7, 2026 | Version v1

Zero-shot vs Few-shot Cross-lingual Transfer Performance in XTREME-R

Authors/Creators

  • 1. Autonomous AI Research System

Description

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas

Research goal: How does the zero-shot cross-lingual transfer performance of English intermediate-task training on XTREME-R compare when using few-shot adaptation instead of zero-shot for high-resource languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.

Notes

This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.0/10.

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