Math Word Problem Solving
61 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
Latest papers
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
Leeroo-AI/mergoo
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We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge.
An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning
Large language models (LLMs) are displaying emergent abilities for math reasoning tasks, and there is a growing attention on enhancing the ability of open-source LLMs through supervised fine-tuning (SFT). In this paper, we aim to explore a general data strategy for supervised data to help optimize and expand math reasoning ability. Firstly, we determine the ability boundary of reasoning paths augmentation by identifying these paths' minimal optimal set. Secondly, we validate that different abilities of the model can be cumulatively enhanced by Mix of Minimal Optimal Sets of corresponding types of data, while our models MMOS achieve SOTA performance on series base models under much lower construction costs. Besides, we point out GSM-HARD is not really hard and today's LLMs no longer lack numerical robustness. Also, we provide an Auto Problem Generator for robustness testing and educational applications. Our code and data are publicly available at https://github. com/cyzhh/MMOS.
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Building on the recent progress in open-source LLMs, our proposed prompting novelty, and some brute-force scaling, we construct OpenMathInstruct-1, a math instruction tuning dataset with 1. 8M problem-solution pairs.
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.
Augmenting Math Word Problems via Iterative Question Composing
The MMIQC dataset is available on the HuggingFace hub at https://huggingface. co/datasets/Vivacem/MMIQC.
Mixtral of Experts
hit-scir/chinese-mixtral-8x7b
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In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
wuhy68/parameter-efficient-moe
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Instruction tuning, a successful paradigm, enhances the ability of LLMs to follow natural language instructions and exhibit robust generalization across a wide range of tasks.
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solutions.
Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning
In this paper, we start with the hypothesis that much smaller LMs, which are weak at multi-step reasoning, can achieve reasonable arithmetic reasoning if arithmetic word problems are posed as a formalize-then-solve task.
KnowledgeMath: Knowledge-Intensive Math Word Problem Solving in Finance Domains
We introduce KnowledgeMath, a novel benchmark designed to evaluate LLMs' capabilities in applying financial knowledge to solve complex math word problems.
