Impact of Intermediate-Task Dataset Size on Calibration Metrics of Multilingual Models in Zero-Shot Cross-Lingual XGLUE Tasks

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 impact of varying the size of the intermediate-task training dataset on the calibration metrics (ECE, Brier score) of multilingual models in zero-shot cross-lingual tasks on the XGLUE benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.