Math Word Problem Solving
80 papers with code • 13 benchmarks • 23 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
MathVista
MathQA
ASDiv
Geometry3K
IconQA
Most implemented papers
LLaMA: Open and Efficient Foundation Language Models
facebookresearch/llama
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arXiv 2023
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
microsoft/DeBERTa
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ICLR 2021
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
Analysing Mathematical Reasoning Abilities of Neural Models
The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes.
Mistral 7B
We introduce Mistral 7B v0. 1, a 7-billion-parameter language model engineered for superior performance and efficiency.
Mixtral of Experts
In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.
Qwen2 Technical Report
qwenlm/qwen2
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This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Measuring Mathematical Problem Solving With the MATH Dataset
hendrycks/math
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To facilitate future research and increase accuracy on MATH, we also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics.
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature.
Large Language Models are Zero-Shot Reasoners
kojima-takeshi188/zero_shot_cot
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Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars.
