Fine-tuning 50B Parameter Models for Cross-lingual Zero-shot Performance on XTREME-R

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 fine-tuning a 50B parameter model on English intermediate language-understanding tasks improve zero-shot cross-lingual F1-scores on the XTREME-R benchmark compared to fine-tuning on a subset of tasks with higher task similarity to target languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.4/10.