Adapter-based Intermediate-Task Training vs Full Fine-Tuning for 500M-Parameter Models in Zero-Shot Cross-Lingual GLUE-X

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 performance of adapter-based intermediate-task training compare to full fine-tuning for 500M-parameter models on the GLUE-X benchmark when evaluated in a zero-shot cross-lingual setting?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.