Computational Overhead of Intermediate-Task Training vs. Direct Fine-Tuning in XTREME-R Benchmark

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 the computational overhead of English intermediate-task training compare to direct fine-tuning on zero-shot cross-lingual tasks in the XTREME-R benchmark when measured in FLOPs and inference latency?

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