Trade-off between Computational Efficiency and Cross-lingual Performance in Intermediate-Task Training on XTREME

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: What is the trade-off between computational efficiency (measured in training FLOPs and inference latency) and cross-lingual performance gains when using intermediate-task training on the XTREME benchmark compared to direct multilingual fine-tuning?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.