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/Jason-J-Choi/DeBERTa_TxtClassifier

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/KU-CVLAB/MoA

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/Leeroo-AI/mergoo

Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

https://github.com/Lightning-AI/lit-gpt

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/Lightning-AI/lit-llama

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/Luohh5/Chain-of-Exemplar

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/MS-P3/code5/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/llama2

LLaMA: Open and Efficient Foundation Language Models

https://github.com/Mind23-2/MindCode-140

LLaMA: Open and Efficient Foundation Language Models

https://github.com/Mind23-2/MindCode-161

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/MindCode-4/code-3/tree/main/dbrx

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

https://github.com/MindCode-4/code-3/tree/main/deberta

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/MindCode-4/code-3/tree/main/deberta_v2

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/MindSpore-scientific-2/code-14/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/NVIDIA/NeMo-Skills

OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data

https://github.com/Owen-Oertell/rlcm

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/QwenLM/Qwen-7B

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/RUCAIBox/LLMBox

PAL: Program-aided Language Models

https://github.com/RamonKaspar/MathPrompter

MathPrompter: Mathematical Reasoning using Large Language Models

https://github.com/Resurgamm/AGP

Adaptive Graph Pruning for Multi-Agent Communication

https://github.com/ShayekhBinIslam/openrag

Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks

https://github.com/ShichaoSun/math_seq2tree

https://github.com/SumbeeLei/Math_EN

Translating a Math Word Problem to an Expression Tree

https://github.com/TIGER-AI-Lab/CritiqueFineTuning

Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate

https://github.com/ZhangYuanhan-AI/NOAH

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/abhaskumarsinha/Corpus2GPT

LLaMA: Open and Efficient Foundation Language Models

https://github.com/aethercortex/llama-x

LLaMA: Open and Efficient Foundation Language Models

https://github.com/agentica-project/verl-pipeline

Self-rewarding correction for mathematical reasoning

https://github.com/ajs7270/placevalue-benchmark

https://github.com/akanyaani/miniLLAMA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/aleemsidra/convlora

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/alibaba-damo-academy/VCBench

Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency

https://github.com/allanj/dynamic-pal

PAL: Program-aided Language Models

https://github.com/allenai/dolma

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/andrewschreiber/hs-math-nlp

Analysing Mathematical Reasoning Abilities of Neural Models

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

Self-Refine: Iterative Refinement with Self-Feedback

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