Intermediate-Task Training Effects on Zero-Shot Cross-Lingual Model Inference Efficiency in XTREME-R

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 intermediate-task training improve the inference efficiency (measured in tokens/sec or latency) of zero-shot cross-lingual models on XTREME-R when evaluated on low-resource languages with varying target task data sizes? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.