SOTAVerified

Arithmetic Reasoning

Papers

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Showing 1–50 of 175 papers

TitleStatusHype
DCR: Quantifying Data Contamination in LLMs EvaluationCode0
DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact VerificationCode0
FinLMM-R1: Enhancing Financial Reasoning in LMM through Scalable Data and Reward Design—0
Mathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic ReasoningCode0
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond—0
DiaBlo: Diagonal Blocks Are Sufficient For FinetuningCode0
VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL—0
Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning—0
Tokenization Constraints in LLMs: A Study of Symbolic and Arithmetic Reasoning Limits—0
HALO: Hierarchical Autonomous Logic-Oriented Orchestration for Multi-Agent LLM SystemsCode1
OMAC: A Broad Optimization Framework for LLM-Based Multi-Agent CollaborationCode0
Fact-Consistency Evaluation of Text-to-SQL Generation for Business Intelligence Using Exaone 3.5—0
CAPO: Cost-Aware Prompt OptimizationCode2
ThoughtProbe: Classifier-Guided Thought Space Exploration Leveraging LLM Intrinsic Reasoning—0
Is the Reversal Curse a Binding Problem? Uncovering Limitations of Transformers from a Basic Generalization FailureCode1
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training—0
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?—0
Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-TuningCode1
Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights—0
Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding—0
On Representational Dissociation of Language and Arithmetic in 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
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training—0
Rethinking Addressing in Language Models via Contexualized Equivariant Positional EncodingCode1
DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models—0
Towards Intrinsic Self-Correction Enhancement in Monte Carlo Tree Search Boosted Reasoning via Iterative Preference Learning—0
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs—0
Hint Marginalization for Improved Reasoning in Large Language Models—0
GaLore+: Boosting Low-Rank Adaptation for LLMs with Cross-Head Projection—0
S^2FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity—0
Think-to-Talk or Talk-to-Think? When LLMs Come Up with an Answer in Multi-Step Arithmetic Reasoning—0
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model—0
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced ReasoningCode0
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
Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language ModelsCode0
Think Beyond Size: Adaptive Prompting for More Effective Reasoning—0
Unlocking Structured Thinking in Language Models with Cognitive Prompting—0
OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction DataCode4
Small Language Models are Equation Reasoners—0
3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and ComposabilityCode0
Relating the Seemingly Unrelated: Principled Understanding of Generalization for Generative Models in Arithmetic Reasoning Tasks—0
Leveraging LLM Reasoning Enhances Personalized Recommender Systems—0
Toward Adaptive Reasoning in Large Language Models with Thought RollbackCode1
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together—0
Qwen2 Technical ReportCode13
Self-training Language Models for Arithmetic ReasoningCode0
SBoRA: Low-Rank Adaptation with Regional Weight UpdatesCode0
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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