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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/microsoft/muzic/tree/main/deeprapper

공식 코드

DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling

https://github.com/microsoft/pengi

공식 코드

Pengi: An Audio Language Model for Audio Tasks

https://github.com/microsoft/pike-rag

공식 코드

PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation

https://github.com/microsoft/promptpex

공식 코드

PromptPex: Automatic Test Generation for Language Model Prompts

https://github.com/microsoft/pycodegpt

공식 코드

When Language Model Meets Private Library

https://github.com/microsoft/reina

공식 코드

Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data

https://github.com/microsoft/rho

공식 코드

Rho-1: Not All Tokens Are What You Need

https://github.com/microsoft/scglab

공식 코드

GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation

https://github.com/microsoft/seed-encoder

공식 코드

Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder

https://github.com/microsoft/semantic_parsing_with_constrained_lm

공식 코드

BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing

https://github.com/microsoft/simxns

공식 코드

Allies: Prompting Large Language Model with Beam Search

https://github.com/microsoft/speecht5

공식 코드

Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data

https://github.com/microsoft/stop

공식 코드

Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation

https://github.com/microsoft/synqo

공식 코드

MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL

https://github.com/microsoft/torchscale

공식 코드

TorchScale: Transformers at Scale

https://github.com/microsoft/uicaption

공식 코드

Lexi: Self-Supervised Learning of the UI Language

https://github.com/microsoft/unilm

공식 코드

Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/microsoft/unilm/blob/master/Diff-Transformer/

공식 코드

Differential Transformer

https://github.com/microsoft/unilm/tree/master/LatentLM

공식 코드

Multimodal Latent Language Modeling with Next-Token Diffusion

https://github.com/microsoft/unilm/tree/master/beit

공식 코드

Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

https://github.com/microsoft/unilm/tree/master/kosmos-2

공식 코드

Kosmos-2: Grounding Multimodal Large Language Models to the World

https://github.com/microsoft/unilm/tree/master/layoutlmv3

공식 코드

LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking

https://github.com/microsoft/unilm/tree/master/trocr

공식 코드

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

https://github.com/microsoft/unispeech

공식 코드

Self-Supervised Learning for speech recognition with Intermediate layer supervision

https://github.com/microsoft/wina

공식 코드

WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference

https://github.com/midiyazhu/micl

공식 코드

Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context Learning

https://github.com/mifei/st-tod

공식 코드

Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems

https://github.com/mikelkn/woah-2025

공식 코드

Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate

https://github.com/mikewangwzhl/zemi

공식 코드

Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks

https://github.com/milankinen/c2w2c

공식 코드

A Character-Word Compositional Neural Language Model for Finnish

https://github.com/mileswyn/ich_mllms_validation

공식 코드

Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping

https://github.com/mindspore-lab/models

공식 코드

SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization

https://github.com/ming-k9/mft

공식 코드

Boosting Large Language Models with Mask Fine-Tuning

https://github.com/mingjianjiang-1/graph-based-uncertainty

공식 코드

Graph-based Uncertainty Metrics for Long-form Language Model Outputs

https://github.com/mingliangliang3/clipf

공식 코드

Frequency Is What You Need: Word-frequency Masking Benefits Vision-Language Model Pre-training

https://github.com/minglllli/clipfit

공식 코드

Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification

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