Comparison of F1 Scores in Multilingual Fine-Tuned Models on XTREME-R Tasks with Mixed-Language vs. English-Only Intermediate

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 F1 score of multilingual fine-tuned models on XTREME-R tasks compare when using mixed-language intermediate-task training data versus English-only intermediate-task training, and does this difference vary by language family?

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