Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
CoRR(2024)
Abstract
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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