Table Question

Table Question Answering (TableQA) focuses on extracting answers from tabular data in response to natural language questions, aiming to bridge the gap between human language and structured information. Current research emphasizes improving model robustness against adversarial perturbations and handling complex scenarios like multi-table queries, ambiguous questions, and low-resource languages, often employing techniques like retrieval-augmented generation, ensemble methods combining text-to-SQL and end-to-end approaches, and reinforcement learning for sequential information retrieval. Advances in TableQA are crucial for enhancing information access and analysis across diverse fields, impacting applications ranging from business intelligence to scientific data exploration.