Scaling Effects of Intermediate-Task Training on 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: Does intermediate-task training with English-only data scale effectively with model size (e.g., XLM-R vs. larger multilingual models like mT5), and how does this scaling compare to multilingual intermediate-task training in terms of XTREME-R performance?

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