2026
LPDG-RAG: Leveraging Probabilistic Rewards and Dynamic Granularity for Retrieval- Augmented Generation
Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by incorporating external documents, but its effectiveness degrades when retrieved passages are irrelevant, incomplete, or inconsistent with parametric knowledge. We present LPDG-RAG, a post-training framework that extends reasoning-based knowle...
Hai-Feng Liu, Rui-Qi Zhao, Nan Zhao et al.
· IEEE Transactions on Audio,... · 0 citations