Cross-lingual Transfer via Intermediate NLI Training for 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: Does intermediate-task training on English NLI datasets improve inference accuracy on non-English XTREME tasks when using LLMs with 10B+ parameters, and how does the performance compare to standard fine-tuning on metrics like F1-score and EM?

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