Semi Structured Table

Semi-structured tables, ubiquitous in diverse data sources, present challenges for automated interpretation and querying due to their inconsistent formats and complex layouts. Current research focuses on improving Large Language Model (LLM) performance on tasks involving these tables, employing techniques like triple extraction, latent diffusion for data augmentation, and context length optimization to enhance accuracy and efficiency. These advancements are crucial for improving information extraction from real-world data sources and enabling more effective applications in fields like regulatory compliance and cross-lingual information synchronization. The development of open-source, generalist LLMs for table-based tasks represents a significant step towards broader accessibility and wider adoption of these methods.