Visual Mathematical AI Reasoning: WE-MATH

Cross-Modal AI systems reasoning on visual mathematical tasks. New research called WE-MATH, July 2024.
The video introduces "WE-MATH," a benchmark designed to evaluate Large Multimodal Models (LMMs) on their ability to perform visual mathematical reasoning. It aims to transcend traditional result-oriented evaluations by focusing on a model's ability to decompose complex problems into simpler sub-problems based on specific knowledge concepts. This benchmark involves a collection of 6.5K visual math problems classified across 67 hierarchical knowledge concepts. To address the evaluation comprehensively, WE-MATH introduces a four-dimensional metric system-Insufficient Knowledge (IK), Inadequate Generalization (IG), Complete Mastery (CM), and Rote Memorization (RM)-which aims to pinpoint specific deficiencies in LMMs' reasoning processes.
A detailed analysis reveals a negative correlation between the complexity of the problem-solving steps and the LMMs' performance, with a notable deterioration in accuracy as the number of required knowledge concepts increases. This trend underscores a significant challenge in LMMs' capacity for knowledge generalization, particularly in transitioning from solving individual sub-problems to integrating these solutions into a coherent whole. The study highlights that while some models, like GPT-4o, show advancements towards better generalization, most still struggle with rote memorization and insufficient knowledge.
Strategic Enhancements of LMMs by the application of a Knowledge Concept Augmentation (KCA) strategy aimed at mitigating the Insufficient Knowledge issue by enriching LMMs with detailed descriptions of necessary concepts. The preliminary results suggest that this approach helps reduce IK but has limited impact on improving generalization capabilities, indicating an area for future research. The overarching goal of WE-MATH is not only to refine current models but also to set a foundation for developing LMMs that can mimic human-like reasoning more closely in complex visual mathematical contexts.
All rights w/ authors:
WE-MATH: Does Your Large Multimodal Model
Achieve Human-like Mathematical Reasoning?
arxiv.org/pdf/2407.01284
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