Math Word Problem Solving
63 papers with code • 11 benchmarks • 17 datasets
A math word problem is a mathematical exercise (such as in a textbook, worksheet, or exam) where significant background information on the problem is presented in ordinary language rather than in mathematical notation. As most word problems involve a narrative of some sort, they are sometimes referred to as story problems and may vary in the amount of technical language used.
Benchmarks
These leaderboards are used to track progress in Math Word Problem Solving
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Libraries
Use these libraries to find Math Word Problem Solving models and implementations
MATH
SVAMP
ASDiv
MathQA
MathVista
IconQA
ALG514
Most implemented papers
PAL: Program-aided Language Models
srush/minichain
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Much of this success can be attributed to prompting methods such as "chain-of-thought'', which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models.
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems
MiuLab/E2EMathSolver
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NAACL 2019
Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions.
Translating a Math Word Problem to an Expression Tree
Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions
lijierui/group-attention
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ACL 2019
Several deep learning models have been proposed for solving math word problems (MWPs) automatically.
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 due to the lack of goal-driven mechanism commonly seen in human problem solving.
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem
IBM/Graph2Tree
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Findings of the Association for Computational Linguistics 2020
In particular, we investigated our model for solving two problems, neural semantic parsing and math word problem.
Graph-to-Tree Learning for Solving Math Word Problems
2003pro/Graph2Tree
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ACL 2020
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 not capture the relationships and order information among the quantities well.
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems
We propose a copy-augmented and feature-enriched sequence to sequence (seq2seq) model, which outperforms existing models by 3. 2% on the Math23K dataset and serves as a strong baseline of the Ape210K dataset.
