A survey of table reasoning with large language models

Xuanliang Zhang, Dingzirui Wang, Longxu Dou, Qingfu Zhu, Wanxiang Che

Abstract: Table reasoning aims to generate inference results based on the user requirement and the provided table. Enhancing the table reasoning capability of the model can aid in obtaining information efficiently. Recent advancements have positioned Large Language Models (LLMs) as the predominant method for table reasoning, primarily due to their substantial reduction in annotation costs and superior performance compared to previous methods. However, existing research still lacks a summary of LLM-based table reasoning works. Therefore, questions about which techniques can improve table reasoning performance in the era of LLMs 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. Also, we provide research directions from the improvement of existing methods to inspire future research.