TabularMath: Understanding Math Reasoning over Tables with Large Language Models

2025-05-26 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

The research introduces TabularMath, a new benchmark designed to evaluate Large Language Models' (LLMs) mathematical reasoning capabilities over tabular data, addressing a gap in existing evaluations focused primarily on math word problems. This benchmark, developed using the AutoT2T neuro-symbolic framework, transforms math word problems into scalable and verifiable tabular reasoning tasks. TabularMath comprises four subsets, featuring both text-based and image-based tables, and assesses performance across dimensions of table complexity, quality, and representation. Key findings indicate that table complexity and reasoning difficulty jointly affect performance, low-quality tables severely compromise LLM reliability, and while different table modalities show similar trends, text-based tables are generally easier for models to process.

Key takeaway

For research scientists developing or evaluating LLMs for business intelligence or similar applications, you should integrate TabularMath into your evaluation pipeline. This will help you identify model vulnerabilities to low-quality or complex tabular data, guiding improvements for robust real-world performance beyond traditional math word problems.

Key insights

TabularMath and AutoT2T enable scalable evaluation of LLMs' math reasoning over diverse, real-world tabular data.

Principles

• Table quality impacts LLM reasoning reliability.
• Complexity and reasoning difficulty are joint performance factors.
• Text-based tables are generally easier for LLMs.

Method

AutoT2T is a neuro-symbolic framework that converts math word problems into scalable, verifiable tabular reasoning tasks, forming the basis for the TabularMath benchmark.

In practice

• Evaluate LLMs on tabular data with varying quality.
• Prioritize text-based table processing for initial tasks.

Topics

• Tabular Math Reasoning
• Large Language Models
• AutoT2T Framework
• TabularMath Benchmark
• Neuro-symbolic AI

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.