Multimodal Model Performance in Low-Resource South Asian Languages with Intermediate-Task Data Augmentation

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 increasing the amount of English intermediate-task training data for multimodal models improve zero-shot performance on XTREME-R metrics for South Asian low-resource languages compared to training on balanced multilingual datasets?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.