Semi Structured

Semi-structured data, encompassing formats like tables, databases, and partially structured documents, presents unique challenges for information extraction and processing. Current research focuses on leveraging large language models (LLMs) and graph-based methods to improve information retrieval, question answering, and document editing from these sources, often incorporating techniques like knowledge graphs, triplet-based prefiltering, and multi-agent systems to enhance accuracy and efficiency. This area is significant because effective handling of semi-structured data is crucial for numerous applications, including legal reasoning, medical diagnosis, and e-commerce, driving the development of more robust and adaptable AI systems. The development of new benchmarks and datasets is also a key focus to facilitate further research and comparison of different approaches.