Fine-tuning Impact on LLaMA-2 Zero-shot Cross-lingual Performance in XTREME-R Adversarial Benchmarks

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 fine-tuning on additional English intermediate tasks impact the zero-shot cross-lingual performance of LLaMA-2 models on XTREME-R adversarial benchmarks compared to Bloom and mBART?

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