3 papers - avg viability 6.7
CompactRAG revolutionizes multi-hop question answering by reducing LLM calls and token overhead, offering a cost-efficient solution for knowledge-intensive reasoning.
Enhancing Retrieval-Augmented Generation systems for improved accuracy and efficiency through innovative test-time strategies.
Optimizing document chunking for Retrieval-Augmented Generation in specialized enterprise domains like oil and gas to improve information retrieval accuracy and reduce computational costs.