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/vcskaushik/LLMzip

LLaMA: Open and Efficient Foundation Language Models

https://github.com/vhellendoorn/code-lms

Evaluating Large Language Models Trained on Code

https://github.com/viethoang1512/kpa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/vilm-ai/viet-llm-eval

Language Models are Few-Shot Learners

https://github.com/vivo-ai-lab/bluelm

Measuring Massive Multitask Language Understanding

https://github.com/vlm-rl/ocean-r1

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

https://github.com/volcengine/vegiantmodel

Language Models are Few-Shot Learners

https://github.com/vvvm23/albert

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

https://github.com/wenet-e2e/west

The Llama 3 Herd of Models

https://github.com/wenlai-lavine/jola

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

https://github.com/willemsenbram/mention-detection-vgd

The Llama 3 Herd of Models

https://github.com/wzzzd/LM_NER

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/x-lance/neusym-rag

Language Models are Few-Shot Learners

https://github.com/xiaoman-zhang/PMC-VQA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xiaoqian19940510/text-classification-

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/xiaoqian19940510/text-classification-surveys

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/xinyooo/ALBERT4Rec

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

https://github.com/xuetianci/pacit

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/xusenlinzy/api-for-open-llm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xverse-ai/xverse-13b

Measuring Massive Multitask Language Understanding

https://github.com/xverse-ai/xverse-7b

Measuring Massive Multitask Language Understanding

https://github.com/xvyaward/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xzhang97666/alpacare

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yahah100/text_summarization

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

https://github.com/yangletliu/finlora

BloombergGPT: A Large Language Model for Finance

https://github.com/yangyucheng000/University/tree/main/model-2/gpt_neox

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

https://github.com/yangyucheng000/University/tree/main/model-2/gpt_neox_japanese

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

https://github.com/yangyucheng000/University/tree/main/model-3/roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text

Scaling Instruction-Finetuned Language Models

https://github.com/ymcui/chinese-mixtral

Mixtral of Experts

https://github.com/young-geng/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yuanmu97/secure-transformer-inference

LLaMA: Open and Efficient Foundation Language Models

https://github.com/zach-zhiling-zheng/reticular_chemist

GPT-4 Technical Report

https://github.com/zaradana/Fast_BERT

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/zchuz/timebench

Scaling Instruction-Finetuned Language Models

https://github.com/zechenli03/sensorllm

The Llama 3 Herd of Models

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