SOTAVerified

Arithmetic Reasoning

Papers

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Showing 101–125 of 175 papers

TitleStatusHype
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?—0
Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights—0
On Representational Dissociation of Language and Arithmetic in Large Language Models—0
Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding—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
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
Think Beyond Size: Adaptive Prompting for More Effective Reasoning—0
Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language ModelsCode0
Unlocking Structured Thinking in Language Models with Cognitive Prompting—0
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
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together—0
Self-training Language Models for Arithmetic ReasoningCode0
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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