SOTAVerified

Arithmetic Reasoning

Papers

Showing 126150 of 175 papers

TitleStatusHype
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic SystemsCode0
A Mechanistic Interpretation of Arithmetic Reasoning in Language Models using Causal Mediation AnalysisCode1
PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuningCode0
Automatic Model Selection with Large Language Models for ReasoningCode1
RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought0
Hint of Thought prompting: an explainable and zero-shot approach to reasoning tasks with LLMs0
Tree of Thoughts: Deliberate Problem Solving with Large Language ModelsCode5
SatLM: Satisfiability-Aided Language Models Using Declarative PromptingCode1
Learning Non-linguistic Skills without Sacrificing Linguistic ProficiencyCode0
CodeT5+: Open Code Large Language Models for Code Understanding and GenerationCode0
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought PromptingCode1
MoT: Memory-of-Thought Enables ChatGPT to Self-ImproveCode1
Self-Evaluation Guided Beam Search for Reasoning0
Progressive-Hint Prompting Improves Reasoning in Large Language ModelsCode2
When do you need Chain-of-Thought Prompting for ChatGPT?0
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language ModelsCode3
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural NetworksCode1
Sparks of Artificial General Intelligence: Early experiments with GPT-4Code6
GPT-4 Technical ReportCode6
MathPrompter: Mathematical Reasoning using Large Language ModelsCode1
LLaMA: Open and Efficient Foundation Language ModelsCode7
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled DataCode1
LEVER: Learning to Verify Language-to-Code Generation with ExecutionCode1
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?Code0
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?Code2
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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