Effect of Multiple Intermediate Tasks on Zero-Shot Cross-Lingual Transfer Performance in 53B Models

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: What is the effect of combining multiple intermediate tasks (versus a single task) on zero-shot cross-lingual transfer performance, as measured by accuracy on the XQuAD benchmark for 53B parameter models?

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