In this paper, we investigate the effectiveness of various LLMs in interpreting tabular data through different prompting strategies and data formats. Our analysis extends 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 influence of representation and prompting on LLM performance. Our study provides insights into the effective use of LLMs on table-related tasks.

翻译：本文系统研究了不同大语言模型通过多样化提示策略与数据格式解析表格数据的有效性。我们针对六个涉及表格任务的基准测试（如问答与事实核查）展开分析，首次评估了大语言模型在基于图像的表格表征上的表现。具体而言，我们比较了五种文本表征与三种图像表征方式，揭示了表征形式和提示方法对大语言模型性能的影响。本研究为有效运用大语言模型处理表格任务提供了重要见解。