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.