Trade-off between Model Size and Intermediate-Task Diversity in Zero-Shot XTREME-R Performance

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 trade-off between model size (100M vs 1B parameters) and intermediate-task diversity affect zero-shot accuracy on XTREME-R tasks when using English intermediate-task training?

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