Table-based reasoning has garnered substantial research interest, particularly in its integration with Large Language Model (LLM) which has revolutionized the general reasoning paradigm. Numerous LLM-based studies introduce symbolic tools (e.g., databases, Python) as assistants to extend human-like abilities in structured table understanding and complex arithmetic computations. However, these studies can be improved better in simulating human cognitive behavior when using symbolic tools, as they still suffer from limitations of non-standard logical splits and constrained operation pools. In this study, we propose PoTable as a novel table-based reasoning method that simulates a human tabular analyst, which integrates a Python interpreter as the real-time executor accompanied by an LLM-based operation planner and code generator. Specifically, PoTable follows a human-like logical stage split and extends the operation pool into an open-world space without any constraints. Through planning and executing in each distinct stage, PoTable standardly completes the entire reasoning process and produces superior reasoning results along with highly accurate, steply commented and completely executable programs. Accordingly, the effectiveness and explainability of PoTable are fully demonstrated. Extensive experiments over three evaluation datasets from two public benchmarks on two backbones show the outstanding performance of our approach. In particular, GPT-based PoTable achieves over 4% higher absolute accuracy than runner-ups on all evaluation datasets.

翻译：基于表格的推理研究已引起广泛关注，特别是与大型语言模型（LLM）的结合彻底革新了通用推理范式。众多基于LLM的研究引入符号化工具（如数据库、Python）作为辅助，以扩展类人的结构化表格理解与复杂算术计算能力。然而，这些研究在模拟人类使用符号工具的认知行为方面仍有改进空间，因其仍受限于非标准化的逻辑划分与受限的操作集合。本研究提出PoTable作为一种新颖的表格推理方法，通过模拟人类表格分析师的工作模式，集成Python解释器作为实时执行器，并辅以基于LLM的操作规划器与代码生成器。具体而言，PoTable遵循类人的逻辑阶段划分，并将操作集合扩展至无约束的开放空间。通过在每个独立阶段进行规划与执行，PoTable以标准化方式完成整个推理流程，不仅生成更优的推理结果，同时产出高精度、具备逐行注释且完全可执行的程序。由此，PoTable的有效性与可解释性得到充分验证。在两个基准测试的三个评估数据集上，基于两种骨干模型的广泛实验表明该方法具有卓越性能。特别地，基于GPT的PoTable在所有评估数据集上均比次优方法实现超过4%的绝对准确率提升。