Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion

Abstract:Retrieval-Augmented Generation has established itself as a fundamental framework in natural language processing, seamlessly integrating information retrieval with the generative capabilities of large language models. However, this process is fundamentally constrained by a critical challenge: semantic space mismatch between queries and retrieved contexts. We propose Knowledge-Aware Semantic Bridging (KASB), a novel framework that improves passage selection quality through semantic space alignment between queries and retrieved documents through intelligent knowledge fusion. Our approach leverages the complementary strengths of generative and retrieval-based knowledge through a multistage process that enhances both relevance and accuracy. We evaluate KASB on three popular open-domain Question Answering datasets to demonstrate the effectiveness of our approach.

Computation and Language (cs.CL)

arXiv:2609.37171 [cs.CL]

(or arXiv:2609.37171v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.37171 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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