NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization

TL;DR

NormTab is a framework that improves LLMs' symbolic reasoning on web tables by normalizing table data, leading to better performance on reasoning tasks involving tabular data.

Contribution

Introducing NormTab, a novel table normalization method that enhances LLMs' symbolic reasoning capabilities on web tables through a preprocessing step.

Findings

Significant performance improvements on WikiTableQuestion and TabFact datasets.

Normalization as a preprocessing step enhances reasoning accuracy.

Web table normalization is crucial for LLM-based symbolic reasoning.

Abstract

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.