
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 scaling the size of pretrained language models (e.g., LLaMA-7B vs. LLaMA-13B) affect the relative improvement gained from mixed intermediate-task training (NLI + QA) in zero-shot cross-lingual retrieval on XTREME-R? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.
This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.3/10.
models, language, affect, scaling, LLaMA-13B, LLaMA-7B, size, pretrained
models, language, affect, scaling, LLaMA-13B, LLaMA-7B, size, pretrained
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