Table Representation

Table representation research focuses on effectively encoding tabular data for machine learning and natural language processing tasks, aiming to improve information extraction, question answering, and data manipulation. Current efforts concentrate on developing flexible formats adaptable to diverse table structures and incorporating contextual information like headers and surrounding text, often leveraging large language models (LLMs) and graph neural networks (GNNs) for enhanced representation learning. These advancements are significant because they enable more robust and efficient processing of tabular data, impacting diverse fields from document understanding and database management to scientific knowledge discovery and spreadsheet automation.