Impact of Intermediate-Task Training on Zero-Shot Cross-Lingual Transfer in Programming-Heavy Languages

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 on English code-related tasks (e.g., code documentation, function description) improve zero-shot cross-lingual transfer performance on XTREME-R tasks, particularly in programming-heavy languages like Python or Java, when measured by code generation accuracy?

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