Table reasoning, which aims to generate the corresponding answer to the question following the user requirement according to the provided table, and optionally a text description of the table, effectively improving the efficiency of obtaining information. Recently, using Large Language Models (LLMs) has become the mainstream method for table reasoning, because it not only significantly reduces the annotation cost but also exceeds the performance of previous methods. However, existing research still lacks a summary of LLM-based table reasoning works. Due to the existing lack of research, questions about which techniques can improve table reasoning performance in the era of LLMs, why LLMs excel at table reasoning, and how to enhance table reasoning abilities in the future, remain largely unexplored. This gap significantly limits progress in research. To answer the above questions and advance table reasoning research with LLMs, we present this survey to analyze existing research, inspiring future work. In this paper, we analyze the mainstream techniques used to improve table reasoning performance in the LLM era, and the advantages of LLMs compared to pre-LLMs for solving table reasoning. We provide research directions from both the improvement of existing methods and the expansion of practical applications to inspire future research.

翻译：表格推理旨在根据提供的表格（可附带表格文本描述）和用户需求，生成对应问题的答案，从而有效提升信息获取效率。近年来，利用大语言模型（LLMs）已成为表格推理的主流方法，这不仅显著降低了标注成本，还超越了以往方法的性能。然而，现有研究仍缺乏对基于LLM的表格推理工作的总结。由于这一研究空白，在LLM时代哪些技术能提升表格推理性能、LLM为何擅长表格推理，以及未来如何增强表格推理能力等问题大多尚未被探索。这一空缺极大限制了研究进展。为解答上述问题并推动LLM表格推理研究，我们通过本综述分析现有研究，以启发未来工作。本文分析了LLM时代用于提升表格推理性能的主流技术，以及LLM相较于前LLM时代在解决表格推理问题上的优势。我们从现有方法改进和实际应用拓展两个方向提供研究思路，以启发未来研究。