AI RESEARCH
RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment
arXiv CS.CL
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ArXi:2604.22520v1 Announce Type: new Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensive. A widely adopted remedy is the hybrid system paradigm, which balances cost and quality by serving most requests with a small model and selectively routing a fraction to a large model.