SOTAVerified

Arithmetic Reasoning

Papers

Showing 151175 of 175 papers

TitleStatusHype
Prompt Space Optimizing Few-shot Reasoning Success with Large Language ModelsCode0
DiaBlo: Diagonal Blocks Are Sufficient For FinetuningCode0
PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuningCode0
DCR: Quantifying Data Contamination in LLMs EvaluationCode0
Overcoming Barriers to Skill Injection in Language Modeling: Case Study in ArithmeticCode0
SBoRA: Low-Rank Adaptation with Regional Weight UpdatesCode0
OpenChat: Advancing Open-source Language Models with Mixed-Quality DataCode0
OMAC: A Broad Optimization Framework for LLM-Based Multi-Agent CollaborationCode0
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic SystemsCode0
Self-training Language Models for Arithmetic ReasoningCode0
3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and ComposabilityCode0
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced ReasoningCode0
CodeT5+: Open Code Large Language Models for Code Understanding and GenerationCode0
ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math QuestionsCode0
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language ModelsCode0
Mathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic ReasoningCode0
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLMCode0
LLM Augmented LLMs: Expanding Capabilities through CompositionCode0
Least-to-Most Prompting Enables Complex Reasoning in Large Language ModelsCode0
Learning Non-linguistic Skills without Sacrificing Linguistic ProficiencyCode0
Improving Arithmetic Reasoning Ability of Large Language Models through Relation Tuples, Verification and Dynamic FeedbackCode0
Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic ReasoningCode0
DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact VerificationCode0
Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language ModelsCode0
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?Code0
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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