Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Sequence of Intermediate-Task Fine-Tuning and Zero-Shot Cross-Lingual Performance on XTREME-R with Adversarial Perturbations

Domain:

natural language processing

Record type:

paper
Creator:
Ass
Publisher:
Zenodo
Host:avatar
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 sequence of intermediate-task fine-tuning in multilingual models affect the zero-shot cross-lingual performance on the XTREME-R benchmark when evaluated on adversarial perturbations? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.6/10.

Visit

doi.org

Tasks

transfer learning

Tags

sequenceintermediate-taskfine-tuningmultilingualmodelsaffectzero-shotcross-lingual

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similar

Intermediate-Task Training for Robust Zero-Shot Cross-Lingual Transfer on Adversarial XTREME-R SamplesSequential Fine-Tuning Task Quantity and Zero-Shot Cross-Lingual Transfer in XTREME-RImpact of Intermediate Task Difficulty on Zero-Shot Cross-Lingual Performance in XTREME-RZero-Shot Cross-Lingual Transfer Performance Variations with Intermediate Task Difficulty in XTREME-RZero-Shot Cross-Lingual Transfer of Intermediate-Task-Trained Models versus Direct Fine-Tuning on XTREME-R for Low-ResourceContrastive-Adversarial Fine-Tuning for Zero-Shot Cross-Lingual Performance in XLM-R

Intermediate-Task Training for Robust Zero-Shot Cross-Lingual Transfer on Adversarial XTREME-R Samples

Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potentia

Sequential Fine-Tuning Task Quantity and Zero-Shot Cross-Lingual Transfer in XTREME-R

Euphemisms are culturally variable and often ambiguous, posing challenges for language models, espec

Impact of Intermediate Task Difficulty on Zero-Shot Cross-Lingual Performance in XTREME-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuni

Zero-Shot Cross-Lingual Transfer Performance Variations with Intermediate Task Difficulty in XTREME-R

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuni

Zero-Shot Cross-Lingual Transfer of Intermediate-Task-Trained Models versus Direct Fine-Tuning on XTREME-R for Low-Resource

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuni

Contrastive-Adversarial Fine-Tuning for Zero-Shot Cross-Lingual Performance in XLM-R

Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potentia