Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering

Applying language models (LMs) to tables is challenging due to the mismatch between the two-dimensional structure of tables and the one-dimensional inputs expected by LMs.This mismatch forces linearization, making LMs particularly sensitive to irrelevant cells.Subtable selection mitigates this challenge by isolating question-relevant content prior to answer generation.However, existing approaches either rely on independent row or column selection, failing to capture cross-row and cross-column dependencies, or attempt global reasoning and face challenges similar to holistic table QA under noisy contexts.We propose PieTa (Piece of Table), a divide-and-conquer subtable selection framework that progressively aggregates locally selected evidence without requiring explicit global reasoning.PieTa uses an iterative, window-based multi-resolution process to construct compact subtables that capture global dependencies while limiting LM exposure to irrelevant content.Extensive experiments demonstrate that PieTa consistently outperforms prior subtable-based and holistic table QA approaches.Our code is available at https://github.com/MLV-group/ Piece-of-Table.git.