Table Question Answering

Table question answering (TQA) focuses on automatically extracting answers from tabular data in response to natural language questions. Current research emphasizes improving the robustness and reasoning capabilities of large language models (LLMs) for TQA, often employing techniques like prompt engineering, retrieval-augmented generation (RAG), and the integration of text-to-SQL and end-to-end approaches. This field is significant because it addresses the challenge of efficiently accessing and interpreting the vast amounts of structured information contained in tables across diverse domains, impacting data analysis, knowledge retrieval, and numerous industrial applications. The development of comprehensive benchmarks and toolkits is also a key area of focus, facilitating more rigorous evaluation and wider adoption of TQA technologies.