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arXiv · 2609.37171

Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion

Xinkai Du·Chao Lv·Yalin Sun·Quanjie Han·Lei Yao·Maosong Sun
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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.

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Xinkai Du, Chao Lv, Yalin Sun, Quanjie Han, Lei Yao, Maosong Sun. 2026-09-29. Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion. https://arxiv.org/abs/2609.37171

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