This paper investigates the question of what makes math word problems (MWPs)
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.

本文研究了大型语言模型在数学问题中面临的挑战，通过对数学问题的语言和数学特征进行深入分析，并训练基于特征的分类器来理解各个特征对数学问题整体难度的影响，并探讨这是否有助于预测大型语言模型在特定类别的数学问题中的表现。

数学阅读理解题为何对语言学习者具挑战性？

What Makes Math Word Problems Challenging for LLMs?

This paper presents a new multi-view RGB-D dataset of nine kitchen scenes,
each containing several objects in realistic cluttered environments including a
subset of objects from the BigBird dataset. The viewpoints of the scenes are
densely sampled and objects in the scenes are annotated with bounding boxes and
in the 3D point cloud. Also, an approach for detection and recognition is
presented, which is comprised of two parts: i) a new multi-view 3D proposal
generation method and ii) the development of several recognition baselines
using AlexNet to score our proposals, which is trained either on crops of the
dataset or on synthetically composited training images. Finally, we compare the
performance of the object proposals and a detection baseline to the Washington
RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset
is more challenging for object detection and recognition. The dataset is
available at: this http URL

介绍了一个新的多视角 RGB-D 数据集，用于目标检测和识别，包括多个识别基准和 AlexNet 模型，证明了该数据集比 Washington RGB-D Scenes 数据集更具挑战性。