Computational Efficiency of English versus Multilingual Intermediate-Task Fine-Tuning on XTREME-R Benchmarks

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 computational efficiency (inference latency and memory usage) of models fine-tuned with English intermediate-task training compare to those with multilingual intermediate-task training when deployed on XTREME-R tasks?

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