Advancing Structured Query Processing in Retrieval-Augmented Generation with Generative Semantic Integration

Retrieval-Augmented Generation (RAG) has become a pivotal approach in enhancing language models by incorporating external knowledge during the text generation process. However, traditional RAG systems often face challenges in processing structured queries, leading to suboptimal integration of retrieved information. In this paper, we introduce a novel method called Generative Semantic Integration (GSI), which advances structured query processing within RAG frameworks. GSI leverages generative models to semantically integrate structured queries with retrieved data, enabling more coherent and contextually relevant responses. Our experiments on benchmark datasets demonstrate that GSI significantly improves the performance of RAG systems in structured query understanding and response generation, outperforming existing baseline models.