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/pierrecolombo/nlg_eval_via_simi_measures

InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation

https://github.com/pingheng001/Cnn-Bert

Semi-supervised Sequence Learning

https://github.com/pipilurj/BONAS

DARTS: Differentiable Architecture Search

https://github.com/pisalore/roberta_results

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/piskovskyi/fMRI-VisionMamba

VMamba: Visual State Space Model

https://github.com/pku-aicare/colacare

ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration

https://github.com/pku-dair/hetu-galvatron

FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism

https://github.com/pku-yuangroup/languagebind

Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

https://github.com/pku-yuangroup/open-sora-plan

Extending Context Window of Large Language Models via Positional Interpolation

https://github.com/pku-yuangroup/video-bench

Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

https://github.com/plachtaa/vall-e-x

AudioLM: a Language Modeling Approach to Audio Generation

https://github.com/pomonam/kronfluence

Studying Large Language Model Generalization with Influence Functions

https://github.com/postech-ami/smile-dataset

Language Models are Few-Shot Learners

https://github.com/pprabh2007/temp

Pre-trained Language Model for Biomedical Question Answering

https://github.com/prajjwal1/adaptive_transformer

Adaptive Attention Span in Transformers

https://github.com/prajjwal1/fluence

Adaptive Attention Span in Transformers

https://github.com/prajjwal1/language-modelling

Regularizing and Optimizing LSTM Language Models

https://github.com/prakhar21/textaugmentation-gpt2

The Curious Case of Neural Text Degeneration

https://github.com/pranav-ust/nlm

Language Modeling with Gated Convolutional Networks

https://github.com/pranay185417/delight

DeLighT: Deep and Light-weight Transformer

https://github.com/pranjalg96/Stylized-Image-captioning

Generating Sentences from a Continuous Space

https://github.com/prasanns/rlhf-length-biases

RLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment

https://github.com/prashil2792/Question-Answering-System-Deep-Learning

End-To-End Memory Networks

https://github.com/prateekstark/retnet

Retentive Network: A Successor to Transformer for Large Language Models

https://github.com/prathameshza/TrOCR_FineTuning

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models

https://github.com/princeton-nlp/mabel

An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models

https://github.com/princeton-nlp/unintentional-unalignment

Discovering Language Model Behaviors with Model-Written Evaluations

https://github.com/printfer/CS6490_NetworkSecurity_FinalProject

DeepWalk: Online Learning of Social Representations

https://github.com/prolearner/hypertorch

Deep Equilibrium Models

https://github.com/protllm/protllm

ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training

https://github.com/pwc-1/Paper-10/tree/main/speech_encoder_decoder

A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition

https://github.com/pwc-1/Paper-10/tree/main/trocr

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models

https://github.com/pwc-1/Paper-5/tree/main/megatron_bert

Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM

https://github.com/pwc-1/Paper-5/tree/main/mistral

Mistral-SPLADE: LLMs for better Learned Sparse Retrieval

https://github.com/pwc-1/Paper-5/tree/main/mixtral

Rethinking LLM Language Adaptation: A Case Study on Chinese Mixtral

https://github.com/pwc-1/Paper-5/tree/main/mluke

mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models

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