  Mathematical modeling involves representing real-world phenomena, systems, or
problems using mathematical expressions and equations to analyze, understand,
and predict their behavior. Given that this process typically requires
experienced experts, there is an interest in exploring whether Large Language
Models (LLMs) can undertake mathematical modeling to potentially decrease human
labor. To evaluate of LLMs in mathematical modeling, we introduce a new
benchmark, Mamo, that transcends traditional result-oriented assessments.
Unlike conventional methods that primarily assess LLMs based on the accuracy of
solutions to mathematical problems, our approach offers deeper insight into the
modeling process itself. By focusing on the processes LLMs undertake rather
than the correctness of their final solutions, Mamo pioneers a novel evaluation
paradigm. This shift underscores the importance of understanding the inherent
modeling capabilities of LLMs, paving the way for a more nuanced and
comprehensive analysis of their problem-solving strategies. Our work marks a
significant advancement in the field, suggesting a new direction for future
research by emphasizing the evaluation of LLMs' modeling processes over the
mere correctness of answers. This benchmark not only facilitates a better
understanding of LLMs' mathematical modeling capabilities but also sets a new
standard for evaluating their performance in complex problem-solving scenarios.
