Intermediate-Task Batch Size Effects on XTREME-R Performance and 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: What is the effect of intermediate-task training batch size (e.g., small vs. large batches) on the inference efficiency and zero-shot cross-lingual transfer performance of encoder-decoder models on XTREME-R, quantified by latency-accuracy trade-offs?

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