In this paper, we present a novel approach for distilling math word problem solving
capabilities from large language models (LLMs) into smaller, more efficient student models …

Geometry problem solving is a well-recognized testbed for evaluating the high-level multi-
modal reasoning capability of deep models. In most existing works, two main geometry …

Recent advances in natural language processing, primarily propelled by Large Language
Models (LLMs), have showcased their remarkable capabilities grounded in in-context …

Recently, deep learning models have made great progress in MWP solving on answer
accuracy. However, they are uninterpretable since they mainly rely on shallow heuristics to …

We propose WorldSense, a benchmark designed to assess the extent to which LLMs are
consistently able to sustain tacit world models, by testing how they draw simple inferences …

Solving math word problem (MWP) with AI techniques has recently made great progress
with the success of deep neural networks (DNN), but it is far from being solved. We argue …

Generating mathematical equations from natural language requires an accurate
understanding of the relations among math expressions. Existing approaches can be …

Solving math word problems depends on how to articulate the problems, the lens through
which models view human linguistic expressions. Real-world settings count on such a …

Solving math word problems (MWP) is a challenging task for natural language processing
systems, as it requires to not only identify and comprehend the problem description within …

Abstract Large Language Models prompting, such as using in-context demonstrations, is a
mainstream technique for invoking LLMs to perform high-performance and solid complex …