Structured Data

Structured data, encompassing tabular data, databases, and knowledge graphs, presents unique challenges for artificial intelligence, particularly in enabling effective reasoning and knowledge extraction. Current research focuses on improving large language models' (LLMs) ability to process and reason with structured data, employing techniques like graph embeddings, hierarchical filtering, and retrieval-augmented generation, often within hybrid LLM/rule-based systems. This work is driven by the need to unlock the vast potential of structured data in diverse applications, ranging from financial analysis and medical diagnostics to scientific knowledge discovery and disaster response. The development of robust benchmarks and foundational models specifically tailored for structured data is a key area of ongoing effort.