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/sultan-hassan/CosmoGemma

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/sunildkumar/lora_from_scratch

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/synthlabsai/big-math

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

https://github.com/tatsu-lab/stanford_alpaca

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/teelinsan/camoscio

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/terrierteam/pyterrier_rag

Search-o1: Agentic Search-Enhanced Large Reasoning Models

https://github.com/thejohncrafter/flows

Lectures on Jacques Herbrand as a Logician

https://github.com/theodb/asr-whisper-finetuning

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/thuml/AutoTimes

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/tiger-ai-lab/general-reasoner

General-Reasoner: Advancing LLM Reasoning Across All Domains

https://github.com/tiger-ai-lab/mammoth

MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

https://github.com/tloen/alpaca-lora

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/turningpoint-ai/visualthinker-r1-zero

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

https://github.com/tutujingyugang1/ChatVLA_public

ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge

https://github.com/uds-lsv/llmft

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/usyd-fsalab/fp6_llm

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/valdecy/pybibx

Gemini: A Family of Highly Capable Multimodal Models

https://github.com/vcskaushik/LLMzip

LLaMA: Open and Efficient Foundation Language Models

https://github.com/vedantgaur/symbolic-mwp-reasoning

Are NLP Models really able to Solve Simple Math Word Problems?

https://github.com/vicentvankor/sun-shine

Qwen2 Technical Report

https://github.com/videocrafter/videocrafter

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/vivo-ai-lab/bluelm

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/vlm-rl/ocean-r1

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

https://github.com/wangrongsheng/caregpt

How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition

https://github.com/wwxu21/cut

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/xiaoman-zhang/PMC-VQA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xuetianci/pacit

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/xusenlinzy/api-for-open-llm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xverse-ai/xverse-13b

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/xvyaward/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xzhang97666/alpacare

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yangyucheng000/University/tree/main/model-1/qwen2

Qwen2 Technical Report

https://github.com/yangyucheng000/University/tree/main/model-3/qwen2_moe

Qwen2 Technical Report

https://github.com/yjiangcm/lion

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/yjiangcm/lte

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/ykwon0407/datainf

LoRA: Low-Rank Adaptation of Large Language Models

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