Cross-lingual Transfer Variability in Intermediate-Task Fine-Tuning Across Model Scales

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 extent of zero-shot cross-lingual transfer from English intermediate-task training vary across different model sizes (e.g., small vs. large LLMs) as measured by performance on XTREME-R and XTREME-S benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.