  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.
