Fine-tuning mT5 batch size effects on zero-shot cross-lingual transfer inference efficiency

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Original abstract

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 mT5 on intermediate English-language tasks with varying batch sizes (e.g., 8, 16, 32) affect its zero-shot cross-lingual transfer inference efficiency (throughput, latency) on XTREME, compared to full-batch fine-tuning? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.

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