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：本文研究了大语言模型在不同提示策略和数据格式下解读表格数据的有效性。我们的分析涵盖六个表格相关任务基准，包括问答和事实核查。我们首次评估了LLMs在基于图像的表格表示上的性能。具体而言，我们比较了五种文本表示和三种图像表示，证明了表示方式和提示对LLM性能的影响。本研究为有效利用LLM处理表格相关任务提供了见解。