Large Language Models (LLMs) often struggle with requests related to information retrieval and data manipulation that frequently arise in real-world scenarios under multiple conditions. In this paper, we demonstrate that leveraging tabular structures in LLM interactions, is more effective than utilizing other structures for handling prevalent requests that operate over factual data. Through comprehensive evaluations across various scenarios and request types, we show that providing tabular structures yields a 40.29\% average performance gain along with better robustness and token efficiency. Through attention-value analysis, we discover that tables help LLMs better locate relevant information, explaining these improvements. Beyond tables and text, we evaluate whether (1) blending structuredness within text, such as providing templates or fixing the order of attributes, and (2) other representative structures, such as knowledge graphs and JSON are helpful. We observe that utilizing tables offers the best balance between efficiency and effectiveness. The method remains robust to task complexity and adapts to unstructured sources through text-to-table conversion. Overall, we highlight the untapped potential of tabular representations for future LLM applications.

翻译：大语言模型在处理现实场景中频繁出现的多条件信息检索与数据操作请求时往往面临困难。本文证明，在处理基于事实数据的常见请求时，利用表格结构进行大语言模型交互，相比其他结构更为有效。通过对多种场景和请求类型的综合评估，我们发现提供表格结构可带来平均40.29%的性能提升，同时具有更好的鲁棒性和标记效率。通过注意力值分析，我们发现表格能帮助大语言模型更准确定位相关信息，从而解释这些改进。除表格和纯文本外，我们还评估了以下两种方式的有效性：（1）在文本中融入结构化特征，例如提供模板或固定属性顺序；（2）使用其他代表性结构，如知识图谱和JSON。实验表明，表格在效率与效果之间实现了最佳平衡。该方法对任务复杂度保持鲁棒性，并能通过文本到表格的转换适应非结构化数据源。总体而言，我们揭示了表格表示在未来大语言模型应用中尚未开发的潜力。