Computational Efficiency of Intermediate-Task Training with Multimodal vs. Text-Only Datasets in Zero-Shot Cross-Lingual OFA

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 computational efficiency (measured in FLOPs and inference latency) of intermediate-task training with multimodal datasets compare to text-only datasets when evaluated on the zero-shot cross-lingual performance of the OFA model on the XTREME-R benchmark?

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