SOTAVerified

Arithmetic Reasoning

Papers

Showing 2650 of 175 papers

TitleStatusHype
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math ReasoningCode2
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical ReasoningCode2
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsCode2
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-VerificationCode2
Scaling Relationship on Learning Mathematical Reasoning with Large Language ModelsCode2
Encouraging Divergent Thinking in Large Language Models through Multi-Agent DebateCode2
Progressive-Hint Prompting Improves Reasoning in Large Language ModelsCode2
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?Code2
Solving Quantitative Reasoning Problems with Language ModelsCode2
Large Language Models are Zero-Shot ReasonersCode2
HALO: Hierarchical Autonomous Logic-Oriented Orchestration for Multi-Agent LLM SystemsCode1
Is the Reversal Curse a Binding Problem? Uncovering Limitations of Transformers from a Basic Generalization FailureCode1
Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-TuningCode1
Rethinking Addressing in Language Models via Contexualized Equivariant Positional EncodingCode1
Arithmetic Without Algorithms: Language Models Solve Math With a Bag of HeuristicsCode1
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation ModelsCode1
Language Imbalance Driven Rewarding for Multilingual Self-improvingCode1
Toward Adaptive Reasoning in Large Language Models with Thought RollbackCode1
An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMsCode1
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive PrinciplesCode1
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor AdaptationCode1
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
Bridging the Gap between Different Vocabularies for LLM EnsembleCode1
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and DistillationCode1
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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