SOTAVerified

Math Word Problem Solving

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.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 107 papers

TitleStatusHype
Math Word Problem Solving with Explicit Numerical ValuesCode1
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem SolvingCode1
Are NLP Models really able to Solve Simple Math Word Problems?Code1
Semantically-Aligned Universal Tree-Structured Solver for Math Word ProblemsCode1
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word ProblemsCode1
Graph-to-Tree Learning for Solving Math Word ProblemsCode1
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word ProblemCode1
A Diversity-Enhanced Knowledge Distillation Model for Practical Math Word Problem SolvingCode0
Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrieval—0
SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented GenerationCode0
When Not to Answer: Evaluating Prompts on GPT Models for Effective Abstention in Unanswerable Math Word Problems—0
Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language ModelsCode0
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement—0
Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment—0
Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving—0
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLMCode0
Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning—0
Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic ReasoningCode0
VerityMath: Advancing Mathematical Reasoning by Self-Verification Through Unit ConsistencyCode0
ATHENA: Mathematical Reasoning with Thought ExpansionCode0
OpenChat: Advancing Open-source Language Models with Mixed-Quality DataCode0
Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models—0
CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?—0
Math Word Problem Solving by Generating Linguistic Variants of Problem StatementsCode0
Does ChatGPT Comprehend the Place Value in Numbers When Solving Math Word Problems?Code0
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation—0
Improving Compositional Generalization in Math Word Problem SolvingCode0
LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced LearningCode0
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbersCode0
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving—0
Towards Interpretable Math Word Problem Solving with Grounded Linguistic Logic Reasoning—0
An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem SolvingCode0
Adversarial Examples for Evaluating Math Word Problem SolversCode0
Generate & Rank: A Multi-task Framework for Math Word Problems—0
Generating Equation by Utilizing Operators : GEO model—0
Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer ModelCode0
A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving—0
Reverse Operation based Data Augmentation for Solving Math Word ProblemsCode0
A Chinese Math Word Problem Solving System Based on Linguistic Theory and Non-statistical Approach—0
A Goal-Driven Tree-Structured Neural Model for Math Word ProblemsCode0
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head AttentionsCode0
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms—0
Analysing Mathematical Reasoning Abilities of Neural ModelsCode0
An Improved Coarse-to-Fine Method for Solving Generation Tasks—0
Translating a Math Word Problem to an Expression TreeCode0
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word ProblemsCode0
Translating a Math Word Problem to a Expression Tree—0
Neural Math Word Problem Solver with Reinforcement Learning—0
Using Intermediate Representations to Solve Math Word Problems—0
Deep Neural Solver for Math Word Problems—0
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All datasetsMATHSVAMPMAWPSMath23KALG514ASDiv-AParaMAWPSDRAW-1KMathQASVAMP (1:N)GSM-PlusMATH minival

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Gemini 2.0 Flash ExperimentalAccuracy89.7—Unverified
2Qwen2.5-Math-72B-Instruct(TIR,Greedy)Accuracy88.1—Unverified
3GPT-4 Turbo (MACM, w/code, voting)Accuracy87.92—Unverified
4Qwen2.5-Math-72B-Instruct(COT,Greedy)Accuracy85.9—Unverified
5Qwen2.5-Math-7B-Instruct(TIR,Greedy)Accuracy85.2—Unverified
6GPT-4-code model (CSV, w/ code, SC, k=16)Accuracy84.3—Unverified
7Qwen2-Math-72B-Instruct(greedy)Accuracy84—Unverified
8Qwen2.5-Math-7B-Instruct(COT,Greedy)Accuracy83.6—Unverified
9Qwen2.5-Math-1.5B-Instruct(TIR,Greedy)Accuracy79.9—Unverified
10OpenMath2-Llama3.1-70B (majority@256)Accuracy79.6—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4 DUPAccuracy94.2—Unverified
2GPT-4 (Teaching-Inspired)Execution Accuracy93.9—Unverified
3GPT-4 (Model Selection)Execution Accuracy93.7—Unverified
4Qwen2(CoT + Code Interpreter)Execution Accuracy92.3—Unverified
5GPT-4 (PHP)Execution Accuracy91.9—Unverified
6OpenMath-CodeLlama-70B (w/ code)Execution Accuracy87.8—Unverified
7MathCoder-L-70BExecution Accuracy84.9—Unverified
8PoT_Eng (self-consistency @ 5)Execution Accuracy83.7—Unverified
9CoT_Eng (self-consistency @ 5)Execution Accuracy82.5—Unverified
10MMOS-CODE-34B(0-shot)Execution Accuracy80.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OpenMath-CodeLlama-70B (w/ code)Accuracy (%)95.7—Unverified
2MsAT-DeductReasonerAccuracy (%)94.3—Unverified
3ATHENA (roberta-large)Accuracy (%)93—Unverified
4Exp-TreeAccuracy (%)92.3—Unverified
5Multi-viewAccuracy (%)92.3—Unverified
6ATHENA (roberta-base)Accuracy (%)92.2—Unverified
7Roberta-DeductReasonerAccuracy (%)92—Unverified
8DeBERTa (PM + VM)Accuracy (%)91—Unverified
9EPTAccuracy (%)88.7—Unverified
10Graph2Tree with RoBERTaAccuracy (%)88.7—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4 (Teaching-Inspired)Accuracy (5-fold)94.3—Unverified
2ATHENA (roberta-large)Accuracy (training-test)86.5—Unverified
3Multi-view* (ours)Accuracy (5-fold)85.2—Unverified
4ATHENA (roberta-base)Accuracy (training-test)84.4—Unverified
5Generate and RankAccuracy (5-fold)84.3—Unverified
6Exp-TreeAccuracy (5-fold)84.1—Unverified
7REAL2: Memory-augmented SolverAccuracy (5-fold)83.18—Unverified
8Roberta-DeductReasonerAccuracy (5-fold)83—Unverified
9MWP-BERTAccuracy (5-fold)82.4—Unverified
10Recall and LearnAccuracy (5-fold)80.8—Unverified