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Large Language Model for Table Processing: A Survey

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arxiv 2402.05121 v3 pith:PASCMVK2 submitted 2024-02-04 cs.AI cs.CL

Weizheng Lu , Jing Zhang , Ju Fan , Zihao Fu , Yueguo Chen , Xiaoyong Du This is my paper · ORCID
classification cs.AIcs.CL
keywords tabletaskslanguagelargeansweringdatalikellms
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance

    cs.CL 2025-06 conditional novelty 6.0 of 10

    MDBench is a synthetically generated, knowledge-guided benchmark for multi-document QA on which frontier LLMs achieve only about 60% exact match.

  2. Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Table-r1 combines a layout-transformation self-supervised task and a mix-paradigm GRPO stage so 7B/8B models outperform other small-model table reasoners and approach GPT-4o-level accuracy.

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