Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching

1. 1Build a routing framework using query decomposition
2. 2Run experiments to evaluate the performance of the framework
3. 3Configure the framework to optimize the trade-off between predictive performance and computational cost
4. 4Test the framework on out-of-distribution data
5. 5Apply the framework to real-world applications

AI engineers and researchers on a team can benefit from this approach to improve the efficiency and accuracy of their LLMs, while data scientists can apply these techniques to real-world problems

💡 Decomposing queries and using historical matching can help avoid the memorization trap and improve generalizability on out-of-distribution data

🤖 Improve LLM routing with query decomposition and historical matching! 💡