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
Qwen2 Technical ReportCode13
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsCode9
Llama 2: Open Foundation and Fine-Tuned Chat ModelsCode8
LLaMA: Open and Efficient Foundation Language ModelsCode7
Sparks of Artificial General Intelligence: Early experiments with GPT-4Code6
Mistral 7BCode6
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-InstructCode5
OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction DataCode4
Mixtral of ExpertsCode4
Let's Verify Step by StepCode4
Galactica: A Large Language Model for ScienceCode4
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning DatasetCode4
PAL: Program-aided Language ModelsCode3
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem SolvingCode3
AlphaMath Almost Zero: Process Supervision without ProcessCode3
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextCode3
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMsCode3
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical ReasoningCode2
Progressive-Hint Prompting Improves Reasoning in Large Language ModelsCode2
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General TasksCode2
DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-SolvingCode2
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math ReasoningCode2
Multi-View Reasoning: Consistent Contrastive Learning for Math Word ProblemCode2
An Empirical Study of Data Ability Boundary in LLMs' Math ReasoningCode2
DeBERTa: Decoding-enhanced BERT with Disentangled AttentionCode2
An Expression Tree Decoding Strategy for Mathematical Equation GenerationCode2
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsCode2
Cumulative Reasoning with Large Language ModelsCode2
GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem SolversCode2
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-VerificationCode2
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical ProblemsCode2
Measuring Mathematical Problem Solving With the MATH DatasetCode2
Solving Quantitative Reasoning Problems with Language ModelsCode2
Large Language Models are Zero-Shot ReasonersCode2
ELASTIC: Numerical Reasoning with Adaptive Symbolic CompilerCode1
Do Multilingual Language Models Think Better in English?Code1
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word ProblemsCode1
Recall and Learn: A Memory-augmented Solver for Math Word ProblemsCode1
MathChat: Converse to Tackle Challenging Math Problems with LLM AgentsCode1
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement LearningCode1
FinanceMath: Knowledge-Intensive Math Reasoning in Finance DomainsCode1
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem SolvingCode1
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word ProblemCode1
Graph-to-Tree Learning for Solving Math Word ProblemsCode1
Automatic Generation of Socratic Subquestions for Teaching Math Word ProblemsCode1
Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word ProblemsCode1
Augmenting Math Word Problems via Iterative Question ComposingCode1
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human AnnotationsCode1
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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