Kernkonzepte
UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG), leverages span-level uncertainty to enhance similarity estimation between text chunks, leading to improved model calibration, robustness, and generalization in long-context tasks.
Li, Z., Xiong, J., Ye, F., Zheng, C., Lu, J., Wan, Z., Wu, X., Liang, X., Li, C., Sun, Z., & Kong, L. (2024). UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation. arXiv preprint arXiv:2410.02719.
This paper introduces UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) that addresses the limitations of existing methods in handling long and semantically disjointed text chunks. The research aims to improve the robustness and generalization of RAG systems by leveraging span-level uncertainty for enhanced similarity estimation between text chunks.