SOTAVerified

Arithmetic Reasoning

Papers

Showing 2650 of 175 papers

TitleStatusHype
CAPO: Cost-Aware Prompt OptimizationCode2
Encouraging Divergent Thinking in Large Language Models through Multi-Agent DebateCode2
Large Language Models are Zero-Shot ReasonersCode2
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-VerificationCode2
Solving Quantitative Reasoning Problems with Language ModelsCode2
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math ReasoningCode2
Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for EnsemblingCode2
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
Scaling Relationship on Learning Mathematical Reasoning with Large Language ModelsCode2
Boosting Language Models Reasoning with Chain-of-Knowledge PromptingCode1
HALO: Hierarchical Autonomous Logic-Oriented Orchestration for Multi-Agent LLM SystemsCode1
An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMsCode1
Batch Prompting: Efficient Inference with Large Language Model APIsCode1
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive PrinciplesCode1
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled DataCode1
MathPrompter: Mathematical Reasoning using Large Language ModelsCode1
Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-TuningCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human AnnotationsCode1
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation ModelsCode1
Learning to Reason for Text Generation from Scientific TablesCode1
Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word ProblemsCode1
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct SolutionsCode1
LEVER: Learning to Verify Language-to-Code Generation with ExecutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Claude 3.5 Sonnet (HPT)Accuracy97.72Unverified
2DUP prompt upon GPT-4Accuracy97.1Unverified
3Qwen2-Math-72B-Instruct (greedy)Accuracy96.7Unverified
4SFT-Mistral-7B (Metamath, OVM, Smart Ensemble)Accuracy96.4Unverified
5OpenMath2-Llama3.1-70B (majority@256)Accuracy96Unverified
6Jiutian-大模型Accuracy95.2Unverified
7DAMOMath-7B(MetaMath, OVM, BS, Ensemble)Accuracy95.1Unverified
8Claude 3 Opus (0-shot chain-of-thought)Accuracy95Unverified
9OpenMath2-Llama3.1-70BAccuracy94.9Unverified
10GPT-4 (Teaching-Inspired)Accuracy94.8Unverified
#ModelMetricClaimedVerifiedStatus
1Text-davinci-002 (175B)(zero-shot-cot)Accuracy78.7Unverified
2Text-davinci-002 (175B) (zero-shot)Accuracy17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Tree of Thoughts (b=5)Success0.74Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4 (Teaching-Inspired)Accuracy92.2Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4 (Teaching-Inspired)Accuracy89.2Unverified