Papers with Code 아카이브

Mathematical Reasoning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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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/aozhongzhang/magr

LLaMA: Open and Efficient Foundation Language Models

https://github.com/arielnlee/Platypus

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/baichuan-inc/Baichuan-Omni-1.5

Qwen2.5 Technical Report

https://github.com/baichuan-inc/baichuan-audio

Qwen2.5 Technical Report

https://github.com/batsresearch/alfred

LLaMA: Open and Efficient Foundation Language Models

https://github.com/bbartoldson/TBA

Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training

https://github.com/beomi/koalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/berniwal/DeepLearningProject

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/bowen-upenn/llm_token_bias

GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

https://github.com/bramiozo/PubScience

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

https://github.com/brandon3964/multimodal-task-vector

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/caraj7/t2i-r1

MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine

https://github.com/chang-github-00/llm-predictive-decoding

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

https://github.com/chaoyi-wu/finetune_llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/chaoyi-wu/pmc-llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/charlesq9/alita

CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets

https://github.com/chenlicodebank/lora_on_analog_hardware

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/clementromac/lamorel

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/cluebenchmark/superclue-math6

SuperCLUE-Math6: Graded Multi-Step Math Reasoning Benchmark for LLMs in Chinese

https://github.com/coastalcph/eu-politics-llms

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/consequentai/fneval

Let's Verify Step by Step

https://github.com/craftjarvis/rat

RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation

https://github.com/damo-nlp-sg/seallms

SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages

https://github.com/davisyoshida/lorax

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/debjitpaul/refiner

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

https://github.com/deepmind/mathematics_dataset

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/dlvuldet/primevul

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

https://github.com/dreamboat26/fuzzy-logic

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/durham/rwkv-finetune-script

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/ecnu-icalk/educhat

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ecolab-postech/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/emrgnt-cmplxty/zero-shot-replication

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

https://github.com/eternityyw/tram-benchmark

PaLM 2 Technical Report

https://github.com/facebookresearch/chai

LLaMA: Open and Efficient Foundation Language Models

https://github.com/facebookresearch/coconut

Training Verifiers to Solve Math Word Problems

https://github.com/facebookresearch/fairseq2

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