Impact of Intermediate-Task Dataset Size on Zero-Shot Cross-Lingual Transfer Performance in Large Language Models

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 choice of intermediate-task dataset size (e.g., 1K vs. 10K samples) affect the zero-shot cross-lingual transfer performance on XTREME-R when fine-tuning large language models like PaLM or Llama, as measured by accuracy and inference latency?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.