Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·25d ago·5 min read
One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG
TL;DR
A new framework called DRAG enables dynamic selection of retriever and generator configurations in Retrieval-Augmented Generation (RAG) systems, improving efficiency and effectiveness. It outperforms static models by adapting to query complexity, reducing inference latency while maintaining high performance.
✦ Why It Matters
Implement DRAG to optimize your RAG systems for better performance and reduced latency today.
Key Takeaways
- Adopt DRAG for dynamic retriever and generator selection.
- Utilize Query Performance Prediction for improved retrieval.
- Fine-tune language models for better configuration predictions.
- Reduce inference latency while maintaining high performance.
How It Works
DRAG operates by analyzing the complexity of incoming queries and selecting the most suitable retriever and generator configurations. The DRAG_QPP approach leverages Query Performance Prediction signals to determine the best retriever, while the DRAG_SFT method fine-tunes a language model to predict optimal configurations based on training data.