Скачать или смотреть LLM Routers Explained!!!

LLM routing offers a solution to this, where each query is first processed by a system that decides which LLM to route it to. Ideally, all queries that can be handled by weaker models should be routed to these models, with all other queries routed to stronger models, minimizing cost while maintaining response quality. However, this turns out to be a challenging problem because the routing system has to infer both the characteristics of an incoming query and different models’ capabilities when routing.


To tackle this, we present RouteLLM, a principled framework for LLM routing based on preference data. We formalize the problem of LLM routing and explore augmentation techniques to improve router performance. We trained four different routers using public data from Chatbot Arena and demonstrate that they can significantly reduce costs without compromising quality, with cost reductions of over 85% on MT Bench, 45% on MMLU, and 35% on GSM8K as compared to using only GPT-4, while still achieving 95% of GPT-4’s performance. We also publicly release all our code and datasets, including a new open-source framework for serving and evaluating LLM routers.

🔗 Links 🔗
RouteLLM: An Open-Source Framework for Cost-Effective LLM Routing
https://lmsys.org/blog/2024-07-01-rou...


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