Structure Aware Retrieval

Structure-aware retrieval focuses on improving information retrieval by leveraging the inherent structure within data, such as the relationships between sentences in a document or nodes in a graph, to enhance the accuracy and efficiency of information extraction. Current research emphasizes integrating structural information into retrieval models using techniques like graph neural networks and hierarchical coarsening, often within the context of retrieval-augmented generation for question answering. This approach addresses limitations of large language models by providing them with more contextually relevant and organized information, leading to improved performance in various tasks, including scientific question answering and open-domain table question answering. The resulting improvements in accuracy and efficiency have significant implications for various fields requiring effective information retrieval from complex datasets.