
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: What is the throughput impact of incorporating code-text multimodal intermediate-task training on XLM-R's inference speed during zero-shot cross-lingual NLI evaluation in XTREME-R, measured in tokens/second? 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.
code-text, training, incorporating, impact, multimodal, intermediate-task, XLM-R, throughput
code-text, training, incorporating, impact, multimodal, intermediate-task, XLM-R, throughput
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