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/uchange/ulangel

Regularizing and Optimizing LSTM Language Models

https://github.com/uclanlp/visualbert

VisualBERT: A Simple and Performant Baseline for Vision and Language

https://github.com/uclnlp/pycodesuggest

Learning Python Code Suggestion with a Sparse Pointer Network

https://github.com/ucsd-dsc-arts/dsc160-final-dsc160_final_group8

Political Speech Generation

https://github.com/uds-lsv/llmft

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

https://github.com/uicdice/imm-language-modeling

Induced Model Matching: How Restricted Models Can Help Larger Ones

https://github.com/uicdice/imm-reinforce

Induced Model Matching: How Restricted Models Can Help Larger Ones

https://github.com/uiuc-focal-lab/syncode

Synchromesh: Reliable code generation from pre-trained language models

https://github.com/ukplab/arxiv2025-inherent-limits-plms

Finetuned Language Models Are Zero-Shot Learners

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

Language Models are Few-Shot Learners

https://github.com/umass-ml4ed/dialogue-kt

Mathfish: Evaluating Language Model Math Reasoning via Grounding in Educational Curricula

https://github.com/umutguneri/Question-Answering-Assistant

End-To-End Memory Networks

https://github.com/unconst/MACH

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

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

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/uppaal/detox-edit

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

https://github.com/upskyy/ContextNet

ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

https://github.com/ut-austin-robin/bumble

BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation

https://github.com/utter-project/elitr-bench

NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark

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

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/uw-mad-dash/pgt_index

Spatial-Temporal Large Language Model for Traffic Prediction

https://github.com/uwnlp/qrn

End-To-End Memory Networks

https://github.com/uzaymacar/comparatively-finetuning-bert

How to Fine-Tune BERT for Text Classification?

https://github.com/valko073/LyricsGANs

Text Generation Based on Generative Adversarial Nets with Latent Variable

https://github.com/vanpersie32/Multigpu-Bert

Semi-supervised Sequence Learning

https://github.com/vanslerry/long_text

Semi-supervised Sequence Learning

https://github.com/vargpt-family/vargpt-v1.1

VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model

https://github.com/varshinireddyt/ULMFiT

Regularizing and Optimizing LSTM Language Models

https://github.com/vatsal199/Obedient_BERT

BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

https://github.com/vectorch-ai/scalellm

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/veegee82/DARTS

DARTS: Differentiable Architecture Search

https://github.com/vermaMachineLearning/Pytorch-JIT-Recurrent-Highway-Network

Recurrent Highway Networks

https://github.com/vganesh46/awd-lstm-pytorch-implementation

Regularizing and Optimizing LSTM Language Models

https://github.com/victor45664/espnet

Improved training of end-to-end attention models for speech recognition

https://github.com/victoriaqiu/10707finalproject

DARTS: Differentiable Architecture Search

https://github.com/viethoang1512/kpa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/viig99/mixmatch-freesound

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

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