  This paper investigates the question of what makes math word problems (MWPs)
in English challenging for large language models (LLMs). We conduct an in-depth
analysis of the key linguistic and mathematical characteristics of MWPs. In
addition, we train feature-based classifiers to better understand the impact of
each feature on the overall difficulty of MWPs for prominent LLMs and
investigate whether this helps predict how well LLMs fare against specific
categories of MWPs.
