Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
<|reference_start|>Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs: 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.<|reference_end|>
@article{deng2024tables,
    title={Tables as Texts or Images: Evaluating the Table Reasoning Ability of
  LLMs and MLLMs},
    author={Naihao Deng, Zhenjie Sun, Ruiqi He, Aman Sikka, Yulong Chen, Lin Ma,
  Yue Zhang, Rada Mihalcea},
    journal={arXiv preprint arXiv:2402.12424},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12424},
    primaryClass={cs.LG cs.AI cs.CL cs.CV}
}