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CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation
Tong Qi, Jingyu Wu, Youbing Yin +3
Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, assembling context sets that may be individually relevant yet collectiv…
DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation
Saadat Hasan Khan, Spencer Hong, Jingyu Wu +4
Retrieval-augmented generation (RAG) is a common technique for grounding language model outputs in domain-specific information. However, RAG is often challenged by reasoning-intens…
LLM Optimization Unlocks Real-Time Pairwise Reranking
Jingyu Wu, Aditya Shrivastava, Jing Zhu +3
Efficiently reranking documents retrieved from information retrieval (IR) pipelines to enhance overall quality of Retrieval-Augmented Generation (RAG) system remains an important y…
Harmonizing Diverse Models: A Layer-wise Merging Strategy for Consistent Generation
Xujun Peng, Anoop Kumar, Jingyu Wu +2
Retrieval-Augmented Generation (RAG) systems leverage Large Language Models (LLMs) to generate accurate and reliable responses that are grounded in retrieved context. However, LLMs…