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Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
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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.
Forward citations
Cited by 2 Pith papers
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MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space
MTabVQA is a new visual multi-table question answering benchmark, and fine-tuning VLMs on its instruction set improves their accuracy on it.
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TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning
An iterative, weakness-guided data synthesis framework for table instruction tuning improves Llama3.1-8B by 11.6 average accuracy points using only 27K synthetic examples.
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