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/punica-ai/punica

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/pwc-1/Paper-8/tree/main/dbrx

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

https://github.com/pwc-1/Paper-8/tree/main/deberta

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/pwc-1/Paper-8/tree/main/deberta_v2

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/pwc-1/Paper-9/tree/main/2/mistral

Mistral 7B

https://github.com/pwc-1/Paper-9/tree/main/2/mixtral

Mixtral of Experts

https://github.com/pwollstadt/IDTxl

Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic

https://github.com/pytorch/opacus

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/qingyun-wu/autogen-eval

MathChat: Converse to Tackle Challenging Math Problems with LLM Agents

https://github.com/qwenlm/qwen

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/qwenlm/qwen-vl

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/qwenlm/qwen1.5

Qwen2 Technical Report

https://github.com/qwopqwop200/GPTQ-for-LLaMa

LLaMA: Open and Efficient Foundation Language Models

https://github.com/r-bakes/math_language_processing

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/radzim/obfuscated

Measuring Mathematical Problem Solving With the MATH Dataset

https://github.com/rah4927/lean-dojo-mew

Measuring Mathematical Problem Solving With the MATH Dataset

https://github.com/reactive-systems/circuit-repair

IsarStep: a Benchmark for High-level Mathematical Reasoning

https://github.com/reactive-systems/ml2

IsarStep: a Benchmark for High-level Mathematical Reasoning

https://github.com/replicate/cog_stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/rijgersberg/geitje

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/rookie-joe/formalalign

Process-Driven Autoformalization in Lean 4

https://github.com/rucaibox/virgo

Enhancing LLM Reasoning with Reward-guided Tree Search

https://github.com/rvlopes/gloria

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

https://github.com/seephys/seephys-project

Skywork-R1V3 Technical Report

https://github.com/shaojintian/power_law_decay

ComplexFormer: Disruptively Advancing Transformer Inference Ability via Head-Specific Complex Vector Attention

https://github.com/shibing624/medicalgpt

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

https://github.com/simula/pointdetectcount

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/skytliang/multi-agents-debate

Large Language Models are Zero-Shot Reasoners

https://github.com/skyworkai/skywork

SkyMath: Technical Report

https://github.com/snucclab/EPT

https://github.com/snucclab/ept-x

https://github.com/song-wx/sift

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/songmzhang/dskd

Knowledge Fusion of Large Language Models

https://github.com/squeezeailab/squeezellm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/srush/minichain

PAL: Program-aided Language Models

https://github.com/stanfordbdhg/llama.cpp

LLaMA: Open and Efficient Foundation Language Models

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