Impact of Intermediate-Task Data Scaling on Multilingual Model Alignment in Zero-Shot Cross-Lingual Settings

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 scaling of intermediate-task training data size impact the alignment of multilingual models in zero-shot cross-lingual settings, as evaluated by human judgment scores on XTREME-R tasks, compared to models trained with smaller intermediate datasets?

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