Large Language Model for Table Processing: A Survey

Abstract

Tables, typically two-dimensional and structured to store large amounts of data, are essential in daily activities like database queries, spreadsheet manipulations, web table question answering, and image table information extraction. Automating these table-centric tasks with Large Language Models (LLMs) or Visual Language Models (VLMs) offers significant public benefits, garnering interest from academia and industry. This survey provides a comprehensive overview of table-related tasks, examining both user scenarios and technical aspects. It covers traditional tasks like table question answering as well as emerging fields such as spreadsheet manipulation and table data analysis. We summarize the training techniques for LLMs and VLMs tailored for table processing. Additionally, we discuss prompt engineering, particularly the use of LLM-powered agents, for various table-related tasks. Finally, we highlight several challenges, including diverse user input when serving and slow thinking using chain-of-thought.

References in corpus (5)

• Survey of Hallucination in Natural Language Generation
• A Survey on Large Language Model based Autonomous Agents
• LayoutLM: Pre-training of Text and Layout for Document Image Understanding
• The Dawn of Natural Language to SQL: Are We Fully Ready?
• Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities

Cited by in corpus (4)

• DataFactory: Collaborative Multi-Agent Framework for Advanced Table Question Answering
• TabNSA: Native Sparse Attention for Efficient Tabular Data Learning
• SQuARE: Structured Query & Adaptive Retrieval Engine For Tabular Formats
• TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models