Dynamic Batch Size Impact on F1-Latency Trade-off in XTREME-R Zero-Shot Transfer

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 dynamic batch size adjustment during intermediate-task fine-tuning impact the trade-off between F1 score and inference latency in zero-shot cross-lingual transfer on the XTREME-R benchmark compared to static batch sizes?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.5/10.