Sequential Fine-Tuning Effects on mT5 Model Efficiency and Performance in Cross-Lingual Euphemism Detection

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 sequential fine-tuning of mT5 models affect inference efficiency (tokens/second) and F1 scores when scaling to even larger variants (e.g., mT5-3B, mT5-11B) for cross-lingual euphemism detection, compared to smaller models like mT5-base? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.8/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.8/10.