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Abstract:Serving various NLP workloads with massive language models is dear: at one
enterprise accomplice, inference prices exceeded $200K/month regardless of over 70% of queries being routine
duties effectively inside the aptitude of smaller fashions.
We current RouteNLP, a closed-loop framework that
routes queries throughout a tiered model portfolio to reduce price
while satisfying per-task quality constraints. The framework integrates three elements: a problem-aware router with shared job-conditioned representations educated on choice
knowledge and high quality signals; confidence-calibrated cascading that uses conformal prediction for distribution-free
threshold initialization; and a distillation-routing co-optimization loop that clusters escalation failures, applies targeted data distillation to cheaper fashions, and robotically retrains the router,
yielding over twice the price improvement of untargeted distillation. 5K queries/day
at an enterprise buyer-service division, RouteNLP reduced inference prices by 58% while sustaining 91% response acceptance
and reducing p99 latency from 1,847 ms to
387 ms. On a six-task benchmark spanning finance, customer service, and legal domains, the framework achieves 40-85%
price reduction whereas retaining 96-100% high quality on structured duties and 96-98% on technology tasks, with
human analysis confirming that 74.5% of routed generation outputs match or exceed frontier-mannequin high quality.
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