表格问答研究综述

Tables serve as essential data carriers, capable of efficiently storing large volumes of high-value information. They are widely used across domains such as economics, finance, scientific research, and more. Table question answering(TableQA) aims to automatically derive answers from tabular data in response to natural language queries, representing a key research direction at the intersection of natural language processing and data analysis. Compared to traditional text-based or knowledge-base question answering, TableQA presents greater challenges, as it requires not only natural language understanding but also the interpretation of two-dimensional table structures, numerical computations, and complex logical reasoning. In recent years, the continuous development of diverse datasets has driven steady progress in TableQA research. The field has evolved from early rule-based and template-based approaches to statistical learning and neural network models, and more recently, to the integration of pre-trained language models, resulting in consistent performance improvements. Notably, the emergence of large language models(LLMs) has ushered in a new phase of development. Leveraging their strong cross-task generalization and reasoning capabilities, LLMs have accelerated innovation and fostered new research paradigms in TableQA. This paper systematically reviews the evolution and representative methods of TableQA, with a particular emphasis on recent advances enabled by LLMs. It also outlines the key challenges currently facing the field and provides a forward-looking perspective on future research directions.