Table Retrieval

Table retrieval focuses on efficiently identifying relevant tables from large datasets to answer questions, a crucial step in open-domain question answering systems. Current research emphasizes improving retrieval accuracy by incorporating join operations across multiple tables, mitigating the impact of noisy training data, and leveraging large language models for enhanced context understanding and response generation. These advancements are driving improvements in the performance of question answering systems and broader applications requiring the extraction of information from structured data sources. The development of more robust and efficient table retrieval methods is vital for advancing natural language processing and knowledge-based systems.