Impact of Code Generation Tasks on Zero-Shot Cross-Lingual Reasoning Accuracy

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 using code generation tasks (e.g., HumanEval-X) as intermediate training steps on zero-shot cross-lingual reasoning accuracy compared to natural language intermediate tasks, as measured by performance on XTREME-R and logical reasoning benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.