The rising capabilities of Language Learning Models are creating unexplored paths.
However, a challenge persists: achieving the desired performance without incurring high costs. RouteLLM, an open-source framework, addresses this issue.
Plot of performance against cost of various LLMs:
Take aways:
- LLM Routing Value: Every query routed to an adequately performing model maximizes cost efficiency and maintains answer quality. This intelligent distribution of tasks makes LLM routing an attractive solution in real-world applications.
- RouteLLM methodology: RouteLLM stands out as a principled framework for LLM routing. By formalizing the routing problem and introducing augmentation techniques, RouteLLM optimizes router performance and achieves cost reductions of up to 85% on certain benchmarks, while maintaining 95% of GPT-4’s performance.
- Data Augmentation: Utilizing preference data has shown effectiveness in training routers, and the application of data augmentation techniques continues to boost performance. The use of public data and LLM judgements strike a balance between effective training and router performance.
References:
- RouteLLM: Learning to Route LLMs with Preference Data, a comprehensive paper delving into the mechanisms of RouteLLM’s capability.
- Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference, an informative discussion on the power of human preference in evaluating LLMs.
- RouteLLM