Table reasoning has shown remarkable progress in a wide range of table-based
tasks. These challenging tasks require reasoning over both free-form natural
language (NL) questions and semi-structured tabular data. However, previous
table reasoning solutions suffer from significant performance degradation on
"huge" tables. In addition, most existing methods struggle to reason over
complex questions since they lack essential information or they are scattered
in different places. To alleviate these challenges, we exploit a table
provider, namely TAP4LLM, on versatile sampling, augmentation, and packing
methods to achieve effective semi-structured data reasoning using large
language models (LLMs), which 1) decompose raw tables into sub-tables with
specific rows or columns based on the rules or semantic similarity; 2) augment
table information by extracting semantic and statistical metadata from raw
tables while retrieving relevant knowledge from trustworthy knowledge sources
(e.g., Wolfram Alpha, Wikipedia); 3) pack sampled tables with augmented
knowledge into sequence prompts for LLMs reasoning while balancing the token
allocation trade-off. We show that TAP4LLM allows for different components as
plug-ins, enhancing LLMs' understanding of structured data in diverse tabular
tasks.

通过使用大型语言模型（LLMs）和 TAP4LLM 表格提供程序，我们在各种表格任务中实现了有效的半结构化数据推理，包括将原始表格分解为子表、提取语义和统计元数据、从可信知识源检索相关知识以及为 LLMs 推理打包采样表格。TAP4LLM 的不同组件作为插件，增强了 LLMs 对多样化表格任务中结构化数据的理解。