
doi: 10.5281/zenodo.21252184 , 10.5281/zenodo.21252183 , 10.5281/zenodo.21446515 , 10.5281/zenodo.21525707 , 10.5281/zenodo.21314649 , 10.5281/zenodo.21326910 , 10.5281/zenodo.21326909 , 10.5281/zenodo.21314650 , 10.5281/zenodo.21725819 , 10.5281/zenodo.21525708 , 10.5281/zenodo.21725820 , 10.5281/zenodo.21446514
doi: 10.5281/zenodo.21252184 , 10.5281/zenodo.21252183 , 10.5281/zenodo.21446515 , 10.5281/zenodo.21525707 , 10.5281/zenodo.21314649 , 10.5281/zenodo.21326910 , 10.5281/zenodo.21326909 , 10.5281/zenodo.21314650 , 10.5281/zenodo.21725819 , 10.5281/zenodo.21525708 , 10.5281/zenodo.21725820 , 10.5281/zenodo.21446514
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 scaling intermediate-task training dataset size affect zero-shot cross-lingual transfer performance on XTREME-R, and what is the optimal balance between dataset size and computational cost? 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.
training, task, scaling, intermediate-task, size, proportionally, improve, zero-shot, English, affect, intermediate, impact, dataset, cross-lingual, transfer
training, task, scaling, intermediate-task, size, proportionally, improve, zero-shot, English, affect, intermediate, impact, dataset, cross-lingual, transfer
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