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/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/aggr/set2set.py

Order Matters: Sequence to sequence for sets

https://github.com/pytorch/pytorch/blob/main/torch/nn/functional.py

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

https://github.com/pytorch/rl/tree/main/examples/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pyy0715/vae-cf-pyorch

Variational Autoencoders for Collaborative Filtering

https://github.com/pzelasko/espnet

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

https://github.com/qapitan/babi-marcus

End-To-End Memory Networks

https://github.com/qhduan/CPM-LM-TF2

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

https://github.com/qiang2100/BERT-LS

LSBert: A Simple Framework for Lexical Simplification

https://github.com/qibinc/KOBE

The Curious Case of Neural Text Degeneration

https://github.com/qihoo360/360zhinao

Effective Long-Context Scaling of Foundation Models

https://github.com/qijindou/CRAB

LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models

https://github.com/qingyun-wu/autogen-eval

Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference

https://github.com/qingzhenduyu/ical

Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer

https://github.com/qinhanmin2014/fine-tune-bert-for-text-classification

How to Fine-Tune BERT for Text Classification?

https://github.com/qitianwu/nodeformer

Matching Networks for One Shot Learning

https://github.com/qiujihao19/artemis

GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest

https://github.com/qizekun/ReCon

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

https://github.com/quangpham2006/SmallPlan

TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model Reasoning

https://github.com/qute012/las-ko

Listen, Attend and Spell

https://github.com/qwenlm/qwen2-vl

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

https://github.com/qwopqwop200/Magneto-pytorch

Foundation Transformers

https://github.com/qywu/PRAL

A Tailored Pre-Training Model for Task-Oriented Dialog Generation

https://github.com/r-three/RAD

Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model

https://github.com/rabiulcste/vqazero

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

https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch

Universal Language Model Fine-tuning for Text Classification

https://github.com/rafaelsandroni/autolabeling

Want To Reduce Labeling Cost? GPT-3 Can Help

https://github.com/rafaljozefowicz/lm

Exploring the Limits of Language Modeling

https://github.com/ragen-ai/vagen

ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL

https://github.com/raghavjajodia/unsupervised_nlu

Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition

https://github.com/rah4927/lean-dojo-mew

Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs

https://github.com/raincleared-song/sparse_gpu_operator

PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU

https://github.com/rajaswa/feedback-and-memory-in-transformers

Addressing Some Limitations of Transformers with Feedback Memory

https://github.com/rajs96/ULMFiT-Twitter-US-Airline-Sentiment

Regularizing and Optimizing LSTM Language Models

https://github.com/rakeshbm/QA-using-Torch

End-To-End Memory Networks

https://github.com/ramanakshay/nanogpt

Language Models are Few-Shot Learners

https://github.com/ranggihwang/pregated_moe

Prefix-Tuning: Optimizing Continuous Prompts for Generation

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