Papers with Code 아카이브

Multi-task Language Understanding

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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/ruc-nlpir/flashrag

REPLUG: Retrieval-Augmented Black-Box Language Models

https://github.com/rvlopes/gloria

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

https://github.com/salesforce/codet5

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/sambanova/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/sanjass/MultiTaskLearning

Measuring Massive Multitask Language Understanding

https://github.com/scrayish/ML_NLP

Language Models are Few-Shot Learners

https://github.com/sdadas/polish-roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/shmsw25/factscore

GPT-4 Technical Report

https://github.com/shreyashankar/gpt3-sandbox

Language Models are Few-Shot Learners

https://github.com/simon-benigeri/narrative-generation

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/smarton-empower/smarton-ai

Language Models are Few-Shot Learners

https://github.com/smile-data/smile

Language Models are Few-Shot Learners

https://github.com/squeezeailab/squeezellm

LLaMA: Open and Efficient Foundation Language Models

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

LLaMA: Open and Efficient Foundation Language Models

https://github.com/superli3/CYRMPR

Evaluating Large Language Models Trained on Code

https://github.com/superli3/codenavi

Evaluating Large Language Models Trained on Code

https://github.com/tatsu-lab/stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/teelinsan/camoscio

LLaMA: Open and Efficient Foundation Language Models

https://github.com/tensorflow/models/tree/master/official/nlp/albert

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

https://github.com/thennal10/fisher-nodes-merging

Merging Models with Fisher-Weighted Averaging

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

Scaling Instruction-Finetuned Language Models

https://github.com/thudm/chatglm

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/thudm/chatglm2-6b

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/thudm/chatglm3

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/thunlp/clueanchor

Atlas: Few-shot Learning with Retrieval Augmented Language Models

https://github.com/tiger-ai-lab/mmlu-pro

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

https://github.com/tighu20/Kaggle-Tweet-Sentiment-Extraction

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/tonyzhaozh/few-shot-learning

Language Models are Few-Shot Learners

https://github.com/traviscoan/cards

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/turkunlp/megatron-deepspeed

Language Models are Few-Shot Learners

https://github.com/turningpoint-ai/visualthinker-r1-zero

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

https://github.com/um-arm-lab/efficient-eng-2-ltl

Language Models are Few-Shot Learners

https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models

GPT-4 Technical Report

https://github.com/usyd-fsalab/fp6_llm

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/utterworks/fast-bert

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/valdecy/pybibx

Gemini: A Family of Highly Capable Multimodal Models

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