Trade-off between Model Size and Inference Efficiency in Zero-shot Cross-lingual NLI on XTREME-R

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 trade-off between model size (XLM-R-Base vs. XLM-R-Large) and inference efficiency (latency, throughput) in zero-shot cross-lingual NLI tasks on XTREME-R after English intermediate-task fine-tuning?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.