The study focuses on improving the performance of large language models (LLMs) in specialized question answering through the development of DSRAG, which utilizes multimodal knowledge graphs. By incorporating diverse information sources such as texts, images, and tables, this framework enhances the reliability of generated responses.
Traditional RAG methods often struggle with domain knowledge accuracy and fail to model context effectively. DSRAG introduces semantic pruning and structured subgraph retrieval, combining knowledge graphs with vector retrieval results, ultimately guiding language models toward superior output. This innovation holds significant implications for various industries relying on domain-specific AI applications, as it can lead to more precise and contextually relevant information retrieval.
👉 Pročitaj original: arXiv AI Papers
