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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/oootttyyy/webarena

Hierarchical Prompting Assists Large Language Model on Web Navigation

https://github.com/open-finance-lab/finlora

BloombergGPT: A Large Language Model for Finance

https://github.com/open-mmlab/mmclassification

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

https://github.com/open-mmlab/mmrazor

DARTS: Differentiable Architecture Search

https://github.com/open3da/ll3da

MotionGPT: Human Motion as a Foreign Language

https://github.com/openaccess-ai-collective/axolotl

NEFTune: Noisy Embeddings Improve Instruction Finetuning

https://github.com/openai/transformer-debugger

Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

https://github.com/openbiolink/promptsource

Language Models are Few-Shot Learners

https://github.com/openbmb/bminf

CPM: A Large-scale Generative Chinese Pre-trained Language Model

https://github.com/openbmb/debater

Repetition Improves Language Model Embeddings

https://github.com/openbmb/minicpm-v

Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages

https://github.com/opendilab/DI-engine

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/opengptx/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/openlmlab/leval

Effective Long-Context Scaling of Foundation Models

https://github.com/openlmlab/moss

CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

https://github.com/openmoss/moss

CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

https://github.com/opennlplab/lightning-attention

TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer

https://github.com/opennlplab/tnn

Toeplitz Neural Network for Sequence Modeling

https://github.com/openrobotlab/ov_parts

A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model

https://github.com/openrobotlab/pointllm

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

https://github.com/openspeech-team/openspeech

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

https://github.com/opensum/bigae

DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining

https://github.com/openvinotoolkit/anomalib

WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

https://github.com/opqrstuvcut/BertMouth

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

https://github.com/optml-group/unlearn-smooth

Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning

https://github.com/oscarknagg/few-shot

Matching Networks for One Shot Learning

https://github.com/osmr/imgclsmob

DARTS: Differentiable Architecture Search

https://github.com/osu-nlp-group/llm-cn-eval

Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study

https://github.com/ovbystrova/InstructionNER

InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER

https://github.com/overwindows/PALM

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

https://github.com/owainwest/uniprot_bert

Semi-supervised Sequence Learning

https://github.com/owennju/mmt-llm

OpenICL: An Open-Source Framework for In-context Learning

https://github.com/padlex/trl

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

https://github.com/paolanu/BERT_epitope

Semi-supervised Sequence Learning

https://github.com/paperspapers/badprompt

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

https://github.com/parajain/data-to-text

Neural Text Generation from Structured Data with Application to the Biography Domain

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