Papers with Code 아카이브

Mathematical Question Answering

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 2
보존된 평가 기록: 10

Building systems that automatically answer mathematical questions.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

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

LLaMA: Open and Efficient Foundation Language Models

https://github.com/mandubian/pytorch_math_dataset

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/meetyou-ai-lab/can-mc-evaluate-llms

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/mesotron/teaching_transformers

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/meta-llama/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/mgmalek/efficient_cross_entropy

https://github.com/microsoft/guidance

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

https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/neuralmind-ai/coliee

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/nicolay-r/reasoning-for-sentiment-analysis-framework

Large Language Models are Zero-Shot Reasoners

https://github.com/ninglab/ecellm

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/nlpxucan/wizardlm

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

https://github.com/ntunlplab/traditional-chinese-alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ofa-sys/expertllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ohadrubin/rpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/openai/minif2f

Measuring Mathematical Problem Solving With the MATH Dataset

https://github.com/openpsi-project/realhf

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

https://github.com/osu-nlp-group/mind2web

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/phoebussi/alpaca-cot

LLaMA: Open and Efficient Foundation Language Models

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

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

Mixtral of Experts

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

MathChat: Converse to Tackle Challenging Math Problems with LLM Agents

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/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/shibing624/medicalgpt

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

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

Large Language Models are Zero-Shot Reasoners

https://github.com/snucclab/EPT

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

https://github.com/squeezeailab/squeezellm

LLaMA: Open and Efficient Foundation Language Models

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