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/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/fairseq2

https://github.com/facebookresearch/minif2f

Measuring Mathematical Problem Solving With the MATH Dataset

https://github.com/facico/chinese-vicuna

LLaMA: Open and Efficient Foundation Language Models

https://github.com/fajri91/indommlu

LLaMA: Open and Efficient Foundation Language Models

https://github.com/flagalpha/llama2-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/freedomintelligence/huatuogpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/fsoft-ai4code/codecapybara

LLaMA: Open and Efficient Foundation Language Models

https://github.com/gair-nlp/abel

Solving Quantitative Reasoning Problems with Language Models

https://github.com/gammatauai/leetcode-hard-gym

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

https://github.com/ganjinzero/rrhf

LLaMA: Open and Efficient Foundation Language Models

https://github.com/gentopia-ai/gentopia

Let's Verify Step by Step

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

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ggml-org/llama.cpp

LLaMA: Open and Efficient Foundation Language Models

https://github.com/glb400/Toy-RecLM

Llama 2: Open Foundation and Fine-Tuned Chat Models

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

LLaMA: Open and Efficient Foundation Language Models

https://github.com/greenbitai/low_bit_llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/guinmoon/llmfarm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/hamishivi/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/hit-scir/chinese-mixtral-8x7b

Mixtral of Experts

https://github.com/hkust-nlp/b-star

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

https://github.com/huberemanuel/DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

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/jingyaogong/minimind

Mixtral of Experts

https://github.com/jlrussin/interpret-math-transformer

Analysing Mathematical Reasoning Abilities of Neural Models

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

Mixtral of Experts

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

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

LLaMA: Open and Efficient Foundation Language Models

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

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

LLaMA: Open and Efficient Foundation Language Models

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