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Language Modeling

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

https://github.com/adriangonz/statistical-nlp-17

Matching Networks for One Shot Learning

https://github.com/adriangrepo/agglomerative_attention_scripts

Agglomerative Attention

https://github.com/ag1988/dss

Efficiently Modeling Long Sequences with Structured State Spaces

https://github.com/agentcoord/agentcoord

ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

https://github.com/agentica-project/verl-pipeline

Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization

https://github.com/agiresearch/openp5

Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

https://github.com/ago109/ContinualPTNCN

Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations

https://github.com/ahclab/naist-simulst

Attention as a Guide for Simultaneous Speech Translation

https://github.com/ahmadelsallab/READ

Universal Language Model Fine-tuning for Text Classification

https://github.com/ahmed-alllam/Direct-Preference-Optimization

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

https://github.com/ahmedalbahnasawy/spec_torch_kaldi-compatibility

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/ahmedelmahy/myownvit

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

https://github.com/ahmedssabir/Textual-Visual-Semantic-Dataset-for-Text-Spotting

Visual Re-ranking with Natural Language Understanding for Text Spotting

https://github.com/ahmedssabir/dataset

Visual Re-ranking with Natural Language Understanding for Text Spotting

https://github.com/ahmetumutdurmus/awd-lstm

Regularizing and Optimizing LSTM Language Models

https://github.com/ahmetumutdurmus/zaremba

Recurrent Neural Network Regularization

https://github.com/ahundt/sharpDARTS

DARTS: Differentiable Architecture Search

https://github.com/ai21labs/lm-evaluation

Language Models are Few-Shot Learners

https://github.com/ai4co/llm-as-hh

Algorithm Evolution Using Large Language Model

https://github.com/ai4co/reevo

Algorithm Evolution Using Large Language Model

https://github.com/aidc-ai/parrot

Ovis: Structural Embedding Alignment for Multimodal Large Language Model

https://github.com/aiot-mlsys-lab/famba-v

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/airKlizz/MsMarco

Passage Re-ranking with BERT

https://github.com/aistairc/kirt_bert_on_abci

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/aithlab/ImprovingGate

Improving the Gating Mechanism of Recurrent Neural Networks

https://github.com/aivolcano/BERT_MRC_CLS

RealFormer: Transformer Likes Residual Attention

https://github.com/akaashdash/kansformers

Scaling Laws for Neural Language Models

https://github.com/akari0216/Paddle-awdlstm

Universal Language Model Fine-tuning for Text Classification

https://github.com/akashe/Python-Code-Generation

Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges

https://github.com/akashsonowal/ddpo-pytorch

Training Diffusion Models with Reinforcement Learning

https://github.com/akirafukui/vqa-mcb

Grounding of Textual Phrases in Images by Reconstruction

https://github.com/akjindal53244/arithmo

NEFTune: Noisy Embeddings Improve Instruction Finetuning

https://github.com/akkarimi/Adversarial-Training-for-ABSA

Adversarial Training for Aspect-Based Sentiment Analysis with BERT

https://github.com/alanansell/bistil

Composable Sparse Fine-Tuning for Cross-Lingual Transfer

https://github.com/alantancr/Hotel-Recommender

Universal Language Model Fine-tuning for Text Classification

https://github.com/albertotestoni/ndq_visual_objects

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

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