Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Robustness Variability in XLM-R and mBERT Under Sequential Fine-Tuning and StressGAN Adversarial Attacks

Domaine:

natural language processing

Type de record:

paper
Créateur:
Ass
Éditeur:
Zenodo
Hôte:avatar
Euphemisms are culturally variable and often ambiguous, posing challenges for language models, especially in low-resource settings. This paper investigates how cross-lingual transfer via sequential fine-tuning affects euphemism detection across five languages: English, Spanish, Chinese, Turkish, and Yoruba. We compare sequential fine-tuning with monolingual and simultaneous fine-tuning using XLM-R and mBERT, analyzing how performance is shaped by language pairings, typological features, and pretraining coverage. Results show that sequential fine-tuning with a high-resource L1 improves L2 perfo Research goal: How does the order of language exposure in sequential fine-tuning affect the robustness of XLM-R and mBERT against StressGAN-generated adversarial examples, measured by F1-score degradation across high-resource (English, Spanish) and low-resource (Yoruba, Turkish) languages? 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

text classificationtransfer learning

Languages

Yoruba

Tags

orderlanguageexposuresequentialfine-tuningaffectrobustnessXLM-R

Licenses

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

Similaires

Trade-offs in Cross-lingual Euphemism Detection: XLM-R vs. mBERT with Sequential and Simultaneous Fine-tuningPerformance Comparison of XLM-R and mBERT on XTREME-R with Sequential vs. Simultaneous Fine-Tuning for Morphologically ComplexGradient-based adversarial fine-tuning for XLM-R robustness in zero-shot cross-lingual classificationCross-lingual Euphemism Detection Accuracy in Sequential XLM-R-Large Fine-tuningSequential Fine-Tuning of XLM-R-Large for Low-Resource Euphemism DetectionContrastive-Adversarial Fine-Tuning for Zero-Shot Cross-Lingual Performance in XLM-R

Trade-offs in Cross-lingual Euphemism Detection: XLM-R vs. mBERT with Sequential and Simultaneous Fine-tuning

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

Performance Comparison of XLM-R and mBERT on XTREME-R with Sequential vs. Simultaneous Fine-Tuning for Morphologically Complex

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

Gradient-based adversarial fine-tuning for XLM-R robustness in zero-shot cross-lingual classification

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

Cross-lingual Euphemism Detection Accuracy in Sequential XLM-R-Large Fine-tuning

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

Sequential Fine-Tuning of XLM-R-Large for Low-Resource Euphemism Detection

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

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