Papers with Code 아카이브

Mathematical Reasoning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

7

평가표

11

보존된 평가 기록

41

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/lxe/simple-llama-finetuner

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/madaan/self-refine

Self-Refine: Iterative Refinement with Self-Feedback

https://github.com/mandubian/pytorch_math_dataset

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/mathllm/math-v

Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset

https://github.com/maxwells-daemons/compositional-learning-experiments

Compositional Generalization with Tree Stack Memory Units

https://github.com/mchl-labs/stambecco

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/meetyou-ai-lab/can-mc-evaluate-llms

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/mesotron/teaching_transformers

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/meta-llama/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/mgmalek/efficient_cross_entropy

Mistral 7B

https://github.com/michelemancusi/LQVAE-separation

Training Verifiers to Solve Math Word Problems

https://github.com/microsoft/guidance

Sparks of Artificial General Intelligence: Early experiments with GPT-4

https://github.com/mistralai/mistral-finetune

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/mistralai/mistral-inference

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/modalminds/mm-eureka

MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable Step-Level Supervision

https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/neuralmind-ai/coliee

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/nicolay-r/reasoning-for-sentiment-analysis-framework

Large Language Models are Zero-Shot Reasoners

https://github.com/ninglab/ecellm

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/nkluge-correa/teenytinyllama

Training Compute-Optimal Large Language Models

https://github.com/nlpxucan/wizardlm

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

https://github.com/ntunlplab/traditional-chinese-alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ofa-sys/expertllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ohadrubin/rpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/openai/minif2f

Measuring Mathematical Problem Solving With the MATH Dataset

https://github.com/opendilab/DI-engine

Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

https://github.com/openpsi-project/realhf

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

https://github.com/oseyosey/ccds

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/osu-nlp-group/mind2web

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/paolo7/shacl2fol

Satisfiability and Containment of Recursive SHACL

https://github.com/phoebussi/alpaca-cot

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/phunterlau/paper_without_code/tree/main/examples/godel-agent

Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement

https://github.com/pilancilab/Riemannian_Preconditioned_LoRA

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/pilancilab/caldera

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/project-numina/aimo-progress-prize

MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning

https://github.com/protagolabs/odyssey-math

MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.