Intermediate-Task Training Effects on XLM-R Inference Efficiency in XTREME-R Benchmarks

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 intermediate-task training with English tasks affect inference efficiency (throughput, latency) of XLM-R on XTREME-R's language understanding tasks compared to no intermediate training, measured using a standardized benchmark (e.g., HuggingFace inference benchmark)?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.