Semantic bridging improves retrieval for question answering systems
Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion
Computation and Language
Summary
Sometimes, computers don’t understand questions and the information they find well enough to give good answers. The authors came up with a way to help computers better match questions with the right background information by combining knowledge from two different methods. This linked understanding makes answers more accurate and relevant. They tested their approach on popular question-answer tasks and found it worked better than before.
What this means in practice
- •For chatbot developers: Improve chatbot answers by selecting background information more closely matching user questions for better response accuracy.
- •For information retrieval engineers: Enhance search systems that combine retrieval with generation by aligning query and document meanings for more relevant results.
Authors
Xinkai Du, Chao Lv, Yalin Sun, Quanjie Han, Lei Yao, Maosong Sun
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.