Scaling of Intermediate-Task Training Benefits in Monolingual and Cross-Lingual Code Generation

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 scaling of intermediate-task training benefits (measured by accuracy gains) compare between monolingual English code generation tasks (e.g., HumanEval, MBPP) and zero-shot cross-lingual code generation (e.g., CodeXGLUE) when using the same intermediate tasks?

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