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

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Showing 1–50 of 107 papers

TitleStatusHype
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
OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction DataCode4
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement—0
Qwen2 Technical ReportCode13
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMsCode3
DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-SolvingCode2
Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment—0
AlphaMath Almost Zero: Process Supervision without ProcessCode3
Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word ProblemsCode1
Toward Self-Improvement of LLMs via Imagination, Searching, and CriticizingCode1
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical ProblemsCode2
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
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextCode3
Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning—0
GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem SolversCode2
An Empirical Study of Data Ability Boundary in LLMs' Math ReasoningCode2
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning DatasetCode4
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsCode9
Augmenting Math Word Problems via Iterative Question ComposingCode1
Mixtral of ExpertsCode4
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General TasksCode2
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human AnnotationsCode1
Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic ReasoningCode0
FinanceMath: Knowledge-Intensive Math Reasoning in Finance DomainsCode1
VerityMath: Advancing Mathematical Reasoning by Self-Verification Through Unit ConsistencyCode0
ATHENA: Mathematical Reasoning with Thought ExpansionCode0
An Expression Tree Decoding Strategy for Mathematical Equation GenerationCode2
Mistral 7BCode6
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math ReasoningCode2
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical ReasoningCode2
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem SolvingCode3
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsCode2
OpenChat: Advancing Open-source Language Models with Mixed-Quality DataCode0
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-InstructCode5
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-VerificationCode2
Cumulative Reasoning with Large Language ModelsCode2
Do Multilingual Language Models Think Better in English?Code1
Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models—0
Llama 2: Open Foundation and Fine-Tuned Chat ModelsCode8
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
MathChat: Converse to Tackle Challenging Math Problems with LLM AgentsCode1
Learning Multi-Step Reasoning by Solving Arithmetic TasksCode1
Let's Verify Step by StepCode4
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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