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/Jason-J-Choi/DeBERTa_TxtClassifier

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/Leeroo-AI/mergoo

Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

https://github.com/Lightning-AI/lit-gpt

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/Lightning-AI/lit-llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/llama2

LLaMA: Open and Efficient Foundation Language Models

https://github.com/Mind23-2/MindCode-140

LLaMA: Open and Efficient Foundation Language Models

https://github.com/Mind23-2/MindCode-161

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/MindCode-4/code-3/tree/main/deberta

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/MindCode-4/code-3/tree/main/deberta_v2

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/MindSpore-scientific-2/code-14/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/NVIDIA/NeMo-Skills

OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data

https://github.com/RUCAIBox/LLMBox

PAL: Program-aided Language Models

https://github.com/ShayekhBinIslam/openrag

Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks

https://github.com/ShichaoSun/math_seq2tree

https://github.com/SumbeeLei/Math_EN

Translating a Math Word Problem to an Expression Tree

https://github.com/abhaskumarsinha/Corpus2GPT

LLaMA: Open and Efficient Foundation Language Models

https://github.com/aethercortex/llama-x

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ajs7270/placevalue-benchmark

https://github.com/akanyaani/miniLLAMA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/allanj/dynamic-pal

PAL: Program-aided Language Models

https://github.com/andrewschreiber/hs-math-nlp

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/aozhongzhang/magr

LLaMA: Open and Efficient Foundation Language Models

https://github.com/batsresearch/alfred

LLaMA: Open and Efficient Foundation Language Models

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/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/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/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/ecnu-icalk/educhat

LLaMA: Open and Efficient Foundation Language Models

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

LLaMA: Open and Efficient Foundation Language Models

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