Inference Throughput of XLM-R-Large in Mixed-Language vs. Monolingual Batches for 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 inference throughput of XLM-R-Large vary when using mixed-language batches versus monolingual batches during sequential fine-tuning for cross-lingual euphemism detection, measured in terms of tokens per second on a standardized benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.