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Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs

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arxiv 2402.12424 v5 pith:PUTNWKP5 submitted 2024-02-19 cs.LG cs.AIcs.CLcs.CV

Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs

Naihao Deng , Zhenjie Sun , Ruiqi He , Aman Sikka , Yulong Chen , Lin Ma , Yue Zhang , Rada Mihalcea This is my paper
classification cs.LG cs.AIcs.CLcs.CV
keywords llmstabledataimage-basedperformancepromptingrepresentationstable-related
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this paper, we investigate the effectiveness of various LLMs in interpreting tabular data through different prompting strategies and data formats. Our analyses extend across six benchmarks for table-related tasks such as question-answering and fact-checking. We introduce for the first time the assessment of LLMs' performance on image-based table representations. Specifically, we compare five text-based and three image-based table representations, demonstrating the role of representation and prompting on LLM performance. Our study provides insights into the effective use of LLMs on table-related tasks.

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Cited by 3 Pith papers

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  1. Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting

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    Introduces TableGrid Navigation (TGN) and Progressive Inference Prompting (PIP) as training-free structured prompting frameworks that improve LLM performance on table question answering over baselines on TableBench an...

  2. CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning

    cs.AI 2026-04 unverdicted novelty 6.0

    CFMS is a coarse-to-fine framework that uses MLLMs to create a multi-perspective knowledge tuple as a reasoning map for symbolic table operations, yielding competitive accuracy on WikiTQ and TabFact.

  3. Task Decomposition for Efficient Annotation

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    Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.