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
Learning Multi-Step Reasoning by Solving Arithmetic TasksCode1
Are NLP Models really able to Solve Simple Math Word Problems?Code1
Do Multilingual Language Models Think Better in English?Code1
ELASTIC: Numerical Reasoning with Adaptive Symbolic CompilerCode1
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement LearningCode1
Recall and Learn: A Memory-augmented Solver for Math Word ProblemsCode1
Semantically-Aligned Universal Tree-Structured Solver for Math Word ProblemsCode1
Learn to Solve Algebra Word Problems Using Quadratic Programming—0
A Chinese Math Word Problem Solving System Based on Linguistic Theory and Non-statistical Approach—0
A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving—0
An Improved Coarse-to-Fine Method for Solving Generation Tasks—0
CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?—0
Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving—0
Deep Neural Solver for Math Word Problems—0
Generate & Rank: A Multi-task Framework for Math Word Problems—0
Generating Equation by Utilizing Operators : GEO model—0
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation—0
Illinois Math Solver: Math Reasoning on the Web—0
Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning—0
Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrieval—0
Learning Fine-Grained Expressions to Solve Math Word Problems—0
Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems—0
Learning to Automatically Solve Algebra Word Problems—0
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation—0
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms—0
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving—0
Neural Math Word Problem Solver with Reinforcement Learning—0
Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment—0
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement—0
Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models—0
Towards Interpretable Math Word Problem Solving with Grounded Linguistic Logic Reasoning—0
Translating a Math Word Problem to a Expression Tree—0
Using Intermediate Representations to Solve Math Word Problems—0
When Not to Answer: Evaluating Prompts on GPT Models for Effective Abstention in Unanswerable Math Word Problems—0
VerityMath: Advancing Mathematical Reasoning by Self-Verification Through Unit ConsistencyCode0
Improving Compositional Generalization in Math Word Problem SolvingCode0
Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic ReasoningCode0
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbersCode0
Reverse Operation based Data Augmentation for Solving Math Word ProblemsCode0
SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented GenerationCode0
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word ProblemsCode0
Does ChatGPT Comprehend the Place Value in Numbers When Solving Math Word Problems?Code0
Translating a Math Word Problem to an Expression TreeCode0
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLMCode0
ATHENA: Mathematical Reasoning with Thought ExpansionCode0
An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem SolvingCode0
Analysing Mathematical Reasoning Abilities of Neural ModelsCode0
Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language ModelsCode0
A Goal-Driven Tree-Structured Neural Model for Math Word ProblemsCode0
Adversarial Examples for Evaluating Math Word Problem SolversCode0
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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