In recent years, Large Language Models (LLMs) have demonstrated remarkable
capabilities in parsing textual data and generating code. However, their
performance in tasks involving tabular data, especially those requiring
symbolic reasoning, faces challenges due to the structural variance and
inconsistency in table cell values often found in web tables. In this paper, we
introduce NormTab, a novel framework aimed at enhancing the symbolic reasoning
performance of LLMs by normalizing web tables. We study table normalization as
a stand-alone, one-time preprocessing step using LLMs to support symbolic
reasoning on tabular data. Our experimental evaluation, conducted on
challenging web table datasets such as WikiTableQuestion and TabFact,
demonstrates that leveraging NormTab significantly improves symbolic reasoning
performance, showcasing the importance and effectiveness of web table
normalization for enhancing LLM-based symbolic reasoning tasks.

该研究介绍了一种名为 NormTab 的新型框架，旨在通过规范化网页表格来提高大型语言模型在符号推理任务中的性能。实验证明，利用 NormTab 显著提高了符号推理性能，为增强基于大型语言模型的符号推理任务的重要性和有效性提供了充分的证据。