AI RESEARCH
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs
arXiv CS.CL
•
ArXi:2604.16937v1 Announce Type: new Translation-based prompting is widely used in multilingual LLMs, yet its effectiveness varies across languages and tasks. We evaluate prompting strategies across ten languages of different resource levels and four benchmarks. Our analysis shows that no single strategy is universally optimal. Translation strongly benefits low-resource languages even when translation quality is imperfect, high-resource languages gain little, and prompt-based self-routing underperforms explicit translation.