Zero-shot Cross-lingual Transfer Performance of XLM-R-large vs. mBERT and mT5 with Adversarial Robustness Training on XTREME-R

Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contex

Research goal: How does the zero-shot cross-lingual transfer performance of XLM-R-large compare to that of other multilingual LLMs like mBERT or mT5 on the XTREME-R benchmark when trained with adversarial robustness methods?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.