Tabular Reasoning

Tabular reasoning focuses on enabling computers to understand and answer questions based on information presented in tables, often in conjunction with accompanying text. Current research heavily utilizes large language models (LLMs), exploring techniques like flexible table formatting, hybrid SQL-text reasoning, and table decomposition to improve accuracy and efficiency, particularly for complex queries spanning multiple tables. This field is significant because it addresses a crucial need for automated information extraction and analysis from the vast amounts of structured data available, impacting diverse applications from question answering systems to medical diagnosis support.