Author

Thomas Butler

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#artificial intelligence Apr 2026

English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training

A systematic, controlled study of the interplay between training language coverage, model scale, and task domain, based on 220 supervised fine-tuning runs on parallel translated multilingual data mixtures spanning mathematical reasoning and API calling tasks, finds that English-only post-training is typically suboptima...

Mehak Preet Dhaliwal, S. Chaurasia, Yao Qin et al. · arXiv.org · 0 citations

Preprint Aug 2026

When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use

It is found that, in this benchmark, supervised fine-tuning (SFT) provides a strong baseline, substantially improving argument language consistency and end-to-end function call accuracy and, under consistent model selection, SFT achieves performance comparable to, and sometimes exceeding more complex reinforcement lear...

Siddharth Chauhan, Thomas Butler, Abhishek Singhania et al. · 0 citations