Papers with Code 아카이브

Multi-task Language Understanding

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 5
보존된 평가 기록: 79

The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. https://arxiv.org/pdf/2009.03300.pdf

코드

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

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

https://github.com/emrgnt-cmplxty/zero-shot-replication

GPT-4 Technical Report

https://github.com/epfml/collaborative-attention

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/eternityyw/tram-benchmark

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ethanjperez/true_few_shot

Language Models are Few-Shot Learners

https://github.com/ethz-privsec/superhuman-ai-consistency

GPT-4 Technical Report

https://github.com/ethz-spylab/superhuman-ai-consistency

GPT-4 Technical Report

https://github.com/expertailab/spaceqa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/facebookresearch/anli

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/facebookresearch/chai

LLaMA: Open and Efficient Foundation Language Models

https://github.com/facebookresearch/fairseq2

https://github.com/facebookresearch/metaicl

UnifiedQA: Crossing Format Boundaries With a Single QA System

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

LLaMA: Open and Efficient Foundation Language Models

https://github.com/few-shot-NER-benchmark/BaselineCode

RoBERTa: A Robustly Optimized BERT Pretraining Approach

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

LLaMA: Open and Efficient Foundation Language Models

https://github.com/flexible-fl/flex-nlp

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/formulamonks/llm-benchmarker-suite

Scaling Instruction-Finetuned Language Models

https://github.com/foundation-model-stack/fms-fsdp

PaLM: Scaling Language Modeling with Pathways

https://github.com/freedomintelligence/huatuogpt

LLaMA: Open and Efficient Foundation Language Models

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

Evaluating Large Language Models Trained on Code

https://github.com/fywalter/label-bias

Language Models are Few-Shot Learners

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

Solving Quantitative Reasoning Problems with Language Models

https://github.com/ganjinzero/rrhf

LLaMA: Open and Efficient Foundation Language Models

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

Language Models are Few-Shot Learners

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

Language Models are Few-Shot Learners

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

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/glouppe/info8010-deep-learning

Evaluating Large Language Models Trained on Code

https://github.com/gmum/dl-mo-2021

Language Models are Few-Shot Learners

https://github.com/google-research/flan

Scaling Instruction-Finetuned Language Models

https://github.com/google/paxml

PaLM: Scaling Language Modeling with Pathways

https://github.com/gpt4life/alpagasus

GPT-4 Technical Report

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

Language Models are Few-Shot Learners

https://github.com/graykode/ALBERT-Pytorch

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

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

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