While Large Language Models (LLMs) are increasingly deployed for table-related tasks, the internal mechanisms enabling them to process linearized two-dimensional structured tables remain opaque. In this work, we investigate the process of table understanding by dissecting the atomic task of cell location. Through activation patching and complementary interpretability techniques, we delineate the table understanding mechanism into a sequential three-stage pipeline: Semantic Binding, Coordinate Localization, and Information Extraction. We demonstrate that models locate the target cell via an ordinal mechanism that counts discrete delimiters to resolve coordinates. Furthermore, column indices are encoded within a linear subspace that allows for precise steering of model focus through vector arithmetic. Finally, we reveal that models generalize to multi-cell location tasks by multiplexing the identical attention heads identified during atomic location. Our findings provide a comprehensive explanation of table understanding within Transformer architectures.

翻译：尽管大型语言模型（LLMs）越来越多地应用于表格相关任务，但其处理线性化二维结构化表格的内部机制仍不透明。本研究通过剖析细胞定位这一原子任务，探究表格理解的过程。借助激活修补及互补的可解释性技术，我们将表格理解机制划分为一个顺序的三阶段流程：语义绑定、坐标定位与信息提取。我们证明模型通过一种序数机制定位目标单元格，该机制通过计数离散分隔符来解析坐标。此外，列索引编码于一个线性子空间内，允许通过向量算术精确引导模型注意力。最后，我们发现模型通过复用原子定位过程中识别的相同注意力头，将能力泛化至多单元格定位任务。我们的研究结果为Transformer架构中的表格理解机制提供了全面解释。