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/hiyouga/llama-efficient-tuning

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/holarissun/rewardmodelingbeyondbradleyterry

Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs

https://github.com/huberemanuel/DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/huggingface/open-r1

s1: Simple test-time scaling

https://github.com/huggingface/transformers

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

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

LLaMA: Open and Efficient Foundation Language Models

https://github.com/idiap/abroad-re

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/if001/rwkv-lm-lora-ja

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/ikergarcia1996/sequence-labeling-llms

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/internlm/xtuner

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/jackaduma/Alpaca-LoRA-RLHF-PyTorch

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/jianguanthu/amor

Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning

https://github.com/jina-ai/thinkgpt

Self-Refine: Iterative Refinement with Self-Feedback

https://github.com/jingyaogong/minimind

Mixtral of Experts

https://github.com/kagnlp/Xolver

Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team

https://github.com/kamanphoebe/look-into-moes

Mixtral of Experts

https://github.com/karpathy/llama2.c

Training Compute-Optimal Large Language Models

https://github.com/kayvr/token-hawk

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kbressem/medalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/knowlab/bi-weekly-paper-presentation

Mistral 7B

https://github.com/krafton-ai/korani

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kvadityasrivatsa/analyzing-llms-for-mwps

Training Verifiers to Solve Math Word Problems

https://github.com/labmlai/annotated_deep_learning_paper_implementations

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/laihuiyuan/mcot

Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations

https://github.com/lean-dojo/LeanAgent

LeanAgent: Lifelong Learning for Formal Theorem Proving

https://github.com/lif31up/attention-is-all-you-need

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/lif31up/extended-BERT-for-low-rank-adaption

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/lighterswang/biomedclip-lora

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/lijierui/group-attention

https://github.com/liucongg/chatglm-finetuning

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/llamafamily/llama-chinese

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/lm-sys/routellm

Training Verifiers to Solve Math Word Problems

https://github.com/loganrjmurphy/leaneuclid

UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression

https://github.com/longhao-chen/aicas2024

LLaMA: Open and Efficient Foundation Language Models

https://github.com/longyuewangdcu/chinese-llama-2

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/lupantech/MathVista

MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

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