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Arithmetic Reasoning

Papers

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Showing 101–125 of 175 papers

TitleStatusHype
ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math QuestionsCode0
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?Code0
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
Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language ModelsCode0
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced ReasoningCode0
LLM Augmented LLMs: Expanding Capabilities through CompositionCode0
Self-training Language Models for Arithmetic ReasoningCode0
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training—0
Leveraging LLM Reasoning Enhances Personalized Recommender Systems—0
Arithmetic Reasoning with LLM: Prolog Generation & Permutation—0
Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering—0
Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning—0
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM—0
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models—0
Can LLMs Maintain Fundamental Abilities under KV Cache Compression?—0
CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization—0
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models—0
Composing Ensembles of Pre-trained Models via Iterative Consensus—0
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models—0
DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models—0
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment—0
Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting—0
Exploring Group and Symmetry Principles in Large Language Models—0
Fact-Consistency Evaluation of Text-to-SQL Generation for Business Intelligence Using Exaone 3.5—0
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All datasetsGSM8KMultiArithGame of 24MathMCMathToF

Benchmark Results

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