Papers with Code 아카이브

Language Modeling

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

https://github.com/yifan-song793/eto

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/yijiuzai/Matching-Networks-for-One-Shot-Learning

Matching Networks for One Shot Learning

https://github.com/yikangshen/megablocks

Mixture of Attention Heads: Selecting Attention Heads Per Token

https://github.com/yinzhangyue/AoR

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

https://github.com/yiyc-kor/bert-study

Semi-supervised Sequence Learning

https://github.com/yiyixuxu/n-grammer-flax

N-Grammer: Augmenting Transformers with latent n-grams

https://github.com/yizhongw/truthfulqa_reeval

TruthfulQA: Measuring How Models Mimic Human Falsehoods

https://github.com/ylacombe/dataspeech

Natural language guidance of high-fidelity text-to-speech with synthetic annotations

https://github.com/ymcui/Chinese-BERT-wwm

Revisiting Pre-Trained Models for Chinese Natural Language Processing

https://github.com/ymcui/Chinese-ELECTRA

Revisiting Pre-Trained Models for Chinese Natural Language Processing

https://github.com/ymcui/Chinese-PreTrained-XLNet

Revisiting Pre-Trained Models for Chinese Natural Language Processing

https://github.com/ymcui/Chinese-XLNet

Revisiting Pre-Trained Models for Chinese Natural Language Processing

https://github.com/ymcui/chinese-llama-alpaca-2

Extending Context Window of Large Language Models via Positional Interpolation

https://github.com/ymcui/chinese-llama-alpaca-3

Rethinking LLM Language Adaptation: A Case Study on Chinese Mixtral

https://github.com/yoavg/bert-syntax

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

https://github.com/yochaiz/darts-UNIQ

DARTS: Differentiable Architecture Search

https://github.com/yonsei-cysec/Language_Model_Memorization

Extracting Training Data from Large Language Models

https://github.com/young-zonglin/bilm-tf-extended

Deep contextualized word representations

https://github.com/younggyoseo/vae-cf-pytorch

Variational Autoencoders for Collaborative Filtering

https://github.com/ypeleg/MinimalIsAllYouNeed

Universal Transformers

https://github.com/yu-yang-li/starglm

Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification

https://github.com/yuanjing-zhu/elmo

Deep contextualized word representations

https://github.com/yuanxiaosc/ELMo

Deep contextualized word representations

https://github.com/yuchuantian/dijiang

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

https://github.com/yufengm/Adaptive

Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning

https://github.com/yuhangT/tf_bert

Semi-supervised Sequence Learning

https://github.com/yuhao318/UP-ViT

Adaptive Input Representations for Neural Language Modeling

https://github.com/yuhengsss/msvmamba

VMamba: Visual State Space Model

https://github.com/yukw777/videoblip

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

https://github.com/yulangz/End-to-End-Memory-Networks-in-Paddle

End-To-End Memory Networks

https://github.com/yulong-XJTU/SimVLM

SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

https://github.com/yun-kwak/decision-transformer-jax

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/yushi-y/dpo-toxic-neurons

A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity

https://github.com/yuxiaw/openfactcheck

RARR: Researching and Revising What Language Models Say, Using Language Models

https://github.com/yuyq96/texthawk

TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and Grounding with 16x Fewer Tokens

https://github.com/yv/rnng

Recurrent Neural Network Grammars

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