Table intelligence with large language models: A comprehensive survey and unified benchmark

Recently, numerous table intelligence (TI) tasks have experienced rapid advancements, driven by the rise of large language models (LLMs). However, these tasks remain highly fragmented and lack a structured and comprehensive synthesis from a global perspective, hindering the identification of overarching trends and challenges. Moreover, this fragmentation prevents the establishment of a systematic evaluation framework in the domain of TI and impedes the accurate assessment of LLMs in TI tasks. To tackle the aforementioned problem, in this survey, we propose a systematic taxonomy encompassing all TI tasks and conduct a comprehensive analysis of existing works. Furthermore, we construct a general benchmark dataset based on TI taxonomy to evaluate the performance of leading LLMs and explore future directions, opportunities, and challenges in TI domain for in-depth research.