Applying language models (LMs) to tables is challenging due to the inherent structural differences between two-dimensional tables and one-dimensional text for which the LMs were originally designed. Furthermore, when applying linearized tables to LMs, the maximum token lengths often imposed in self-attention calculations make it difficult to comprehensively understand the context spread across large tables. To address these challenges, we present PieTa (Piece of Table), a new framework for sub-table-based question answering (QA). PieTa operates through an iterative process of dividing tables into smaller windows, using LMs to select relevant cells within each window, and merging these cells into a sub-table. This multi-resolution approach captures dependencies across multiple rows and columns while avoiding the limitations caused by long context inputs. Instantiated as a simple iterative sub-table union algorithm, PieTa demonstrates improved performance over previous sub-table-based QA approaches.

翻译：将语言模型应用于表格处理具有挑战性，因为二维表格与语言模型原本设计处理的一维文本存在固有的结构差异。此外，当将线性化表格输入语言模型时，自注意力计算中常设的最大标记长度限制，使得模型难以全面理解分布在大型表格中的上下文信息。为应对这些挑战，我们提出PieTa（表格分片）——一种基于子表的问答新框架。PieTa通过迭代过程运行：先将表格分割为较小窗口，利用语言模型在每个窗口中选择相关单元格，再将这些单元格合并为子表。这种多分辨率方法能够捕捉跨多行多列的依赖关系，同时规避长上下文输入带来的限制。通过实例化为简单的迭代子表合并算法，PieTa在性能上超越了以往基于子表的问答方法。