GPU-hour efficiency comparison of zero-shot cross-lingual transfer and few-shot fine-tuning in XLM-RoBERTa-base on XTREME-R

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 GPU-hour efficiency of zero-shot cross-lingual transfer with intermediate-task training compare to that of few-shot fine-tuning when evaluated on XTREME-R using XLM-RoBERTa-base?

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