TabularMath: Understanding Math Reasoning over Tables with Large Language Models

TL;DR

This paper introduces TabularMath, a comprehensive benchmark for evaluating large language models' reasoning over tables, addressing real-world challenges like incomplete data and diverse table formats.

Contribution

It presents AutoT2T, a neuro-symbolic framework for transforming math problems into scalable tabular reasoning tasks, and develops TabularMath, a multi-faceted benchmark for this purpose.

Findings

Table complexity and reasoning difficulty jointly affect performance.

Low-quality tables significantly hinder reasoning accuracy.

Text-based tables are generally easier for models to interpret.

Abstract

Mathematical reasoning has long been a key benchmark for evaluating large language models. Although substantial progress has been made on math word problems, the need for reasoning over tabular data in real-world applications has been overlooked. For instance, applications such as business intelligence demand not only multi-step numerical reasoning with tables but also robustness to incomplete or inconsistent information. However, comprehensive evaluation in this area is severely limited, constrained by the reliance on manually collected tables that are difficult to scale and the lack of coverage for potential traps encountered in real-world scenarios. To address this problem, we propose AutoT2T, a neuro-symbolic framework that controllably transforms math word problems into scalable and verified tabular reasoning tasks. Building on this pipeline, we develop TabularMath, a benchmark comprising four subsets that include both text-based and image-based tables, covering table complexity, table quality, and table representation dimensions. Our study reveals three key observations: (1) Table complexity and reasoning difficulty impact reasoning performance jointly; (2) Low-quality tables pose severe risks to reliable reasoning in current LLMs; (3) Different table modalities show similar trends, with text-based tables typically being easier for models to reason over. In-depth analyses are conducted for each observation to guide future research.