# Math Word Problem Solving

32 papers with code • 8 benchmarks • 12 datasets

## Most implemented papers

# LLaMA: Open and Efficient Foundation Language Models

We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.

# 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.

# Measuring Mathematical Problem Solving With the MATH Dataset

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.

# Are NLP Models really able to Solve Simple Math Word Problems?

Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered "solved" with the bulk of research attention moving to more complex MWPs.

# LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning

To address this issue and make a step towards interpretable MWP solving, we first construct a high-quality MWP dataset named InterMWP which consists of 11, 495 MWPs and annotates interpretable logical formulas based on algebraic knowledge as the grounded linguistic logic of each solution equation.

# Toolformer: Language Models Can Teach Themselves to Use Tools

Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale.

# Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems

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

Several deep learning models have been proposed for solving math word problems (MWPs) automatically.