A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges

TL;DR

This survey comprehensively reviews recent advancements, benchmarks, methodologies, and challenges in multimodal large language models' mathematical reasoning, highlighting key developments and future directions for achieving artificial general intelligence.

Contribution

It provides the first extensive analysis of Math-LLMs in multimodal settings, categorizing research into benchmarks, methods, and challenges, and identifying key obstacles for future progress.

Findings

Reviewed over 200 studies since 2021 on Math-LLMs

Categorized the field into benchmarks, methodologies, and challenges

Identified five major challenges hindering AGI development in this domain

Abstract

Mathematical reasoning, a core aspect of human cognition, is vital across many domains, from educational problem-solving to scientific advancements. As artificial general intelligence (AGI) progresses, integrating large language models (LLMs) with mathematical reasoning tasks is becoming increasingly significant. This survey provides the first comprehensive analysis of mathematical reasoning in the era of multimodal large language models (MLLMs). We review over 200 studies published since 2021, and examine the state-of-the-art developments in Math-LLMs, with a focus on multimodal settings. We categorize the field into three dimensions: benchmarks, methodologies, and challenges. In particular, we explore multimodal mathematical reasoning pipeline, as well as the role of (M)LLMs and the associated methodologies. Finally, we identify five major challenges hindering the realization of AGI in this domain, offering insights into the future direction for enhancing multimodal reasoning capabilities. This survey serves as a critical resource for the research community in advancing the capabilities of LLMs to tackle complex multimodal reasoning tasks.