Gradient Accumulation Effects on XLM-R Zero-Shot GLUE Performance and Efficiency Trade-offs

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 gradient accumulation during intermediate-task training impact the zero-shot performance of XLM-R on the GLUE benchmark compared to true large-batch training, and what is the trade-off between efficiency and downstream task accuracy?

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