  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.
