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A survey of deep learning for mathematical reasoning
Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in
various fields, including science, engineering, finance, and everyday life. The development …
various fields, including science, engineering, finance, and everyday life. The development …
From lsat: The progress and challenges of complex reasoning
Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark
of human intelligence, it involves a degree of explicit reading comprehension, interpretation …
of human intelligence, it involves a degree of explicit reading comprehension, interpretation …
Inter-GPS: Interpretable geometry problem solving with formal language and symbolic reasoning
Geometry problem solving has attracted much attention in the NLP community recently. The
task is challenging as it requires abstract problem understanding and symbolic reasoning …
task is challenging as it requires abstract problem understanding and symbolic reasoning …
GeoQA: A geometric question answering benchmark towards multimodal numerical reasoning
Automatic math problem solving has recently attracted increasing attention as a long-
standing AI benchmark. In this paper, we focus on solving geometric problems, which …
standing AI benchmark. In this paper, we focus on solving geometric problems, which …
DREAM: A challenge data set and models for dialogue-based reading comprehension
We present DREAM, the first dialogue-based multiple-choice reading comprehension data
set. Collected from English as a Foreign Language examinations designed by human …
set. Collected from English as a Foreign Language examinations designed by human …
Graph-to-tree learning for solving math word problems
While the recent tree-based neural models have demonstrated promising results in
generating solution expression for the math word problem (MWP), most of these models do …
generating solution expression for the math word problem (MWP), most of these models do …
Generate & rank: A multi-task framework for math word problems
Math word problem (MWP) is a challenging and critical task in natural language processing.
Many recent studies formalize MWP as a generation task and have adopted sequence-to …
Many recent studies formalize MWP as a generation task and have adopted sequence-to …
[PDF][PDF] A goal-driven tree-structured neural model for math word problems.
Most existing neural models for math word problems exploit Seq2Seq model to generate
solution expressions sequentially from left to right, whose results are far from satisfactory …
solution expressions sequentially from left to right, whose results are far from satisfactory …
Template-based math word problem solvers with recursive neural networks
The design of automatic solvers to arithmetic math word problems has attracted
considerable attention in recent years and a large number of datasets and methods have …
considerable attention in recent years and a large number of datasets and methods have …
Let gpt be a math tutor: Teaching math word problem solvers with customized exercise generation
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 …
capabilities from large language models (LLMs) into smaller, more efficient student models …