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

• URL: http://arxiv.org/abs/2412.07629v4
• Date: Wed, 19 Feb 2025 11:56:57 GMT
• Title: Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering
• Authors: Wonjin Lee, Kyumin Kim, Sungjae Lee, Jihun Lee, Kwang In Kim,
• Abstract summary: PieTa is a new framework for subtable-based question answering (QA) It 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 subtable. It demonstrates improved performance over previous subtable-based QA approaches.
• Score: 20.926770550682964
• License:
• Abstract: 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 subtable-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 subtable. 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 subtable union algorithm, PieTa demonstrates improved performance over previous subtable-based QA approaches.

Related papers

• HiddenTables & PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies [9.09415727445941]
  We propose a cooperative game dubbed "HiddenTables" as a potential resolution to this challenge. "HiddenTables" is played between the code-generating "r" and the "Oracle windows" which evaluates the ability of the agents to solve Table QA tasks. We provide evidential experiments on a diverse set of tables that demonstrate an LLM's collective inability to generalize and perform on complex queries.
  arXiv  Detail & Related papers  (2024-06-16T04:53:29Z)
• Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval [52.592071689901196]
  We introduce a method that uncovers useful join relations for any query and database during table retrieval. Our method outperforms the state-of-the-art approaches for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up to 5.4% in accuracy.
  arXiv  Detail & Related papers  (2024-04-15T15:55:01Z)
• Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion [57.53174887650989]
  Table question answering is a popular task that assesses a model's ability to understand and interact with structured data. Existing methods either convert both the table and external knowledge into text, which neglects the structured nature of the table. We propose a simple yet effective method to integrate external information in a given table.
  arXiv  Detail & Related papers  (2024-01-28T03:37:11Z)
• Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding [79.9461269253121]
  We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks.
  arXiv  Detail & Related papers  (2024-01-09T07:46:26Z)
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning [55.33939289989238]
  We propose TAP4LLM as a versatile pre-processor suite for leveraging large language models (LLMs) in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding.
  arXiv  Detail & Related papers  (2023-12-14T15:37:04Z)
• MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering [61.48881995121938]
  Real-world queries are complex in nature, often over multiple tables in a relational database or web page. Our model, MultiTabQA, not only answers questions over multiple tables, but also generalizes to generate tabular answers.
  arXiv  Detail & Related papers  (2023-05-22T08:25:15Z)
• SEMv2: Table Separation Line Detection Based on Instance Segmentation [96.36188168694781]
  We propose an accurate table structure recognizer, termed SEMv2 (SEM: Split, Embed and Merge) We address the table separation line instance-level discrimination problem and introduce a table separation line detection strategy based on conditional convolution. To comprehensively evaluate the SEMv2, we also present a more challenging dataset for table structure recognition, dubbed iFLYTAB.
  arXiv  Detail & Related papers  (2023-03-08T05:15:01Z)
• Retrieving Complex Tables with Multi-Granular Graph Representation Learning [20.72341939868327]
  The task of natural language table retrieval seeks to retrieve semantically relevant tables based on natural language queries. Existing learning systems treat tables as plain text based on the assumption that tables are structured as dataframes. We propose Graph-based Table Retrieval (GTR), a generalizable NLTR framework with multi-granular graph representation learning.
  arXiv  Detail & Related papers  (2021-05-04T20:19:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.