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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/LMOps/tree/main/llma

공식 코드

Inference with Reference: Lossless Acceleration of Large Language Models

https://github.com/microsoft/MASS

공식 코드

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/microsoft/MPNet

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MPNet: Masked and Permuted Pre-training for Language Understanding

https://github.com/microsoft/Multilingual-Culture-First-Misgendering-Guardrails

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A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications

https://github.com/microsoft/ProphetNet

공식 코드

Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise

https://github.com/microsoft/SafeNLP

공식 코드

An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models

https://github.com/microsoft/Samba

공식 코드

Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

https://github.com/microsoft/TAP

공식 코드

TAP: Text-Aware Pre-training for Text-VQA and Text-Caption

https://github.com/microsoft/Table-Pretraining

공식 코드

TAPEX: Table Pre-training via Learning a Neural SQL Executor

https://github.com/microsoft/UniTAB

공식 코드

UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling

https://github.com/microsoft/UniVL

공식 코드

UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation

https://github.com/microsoft/Unicoder

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XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge

https://github.com/microsoft/alympics

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ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents

https://github.com/microsoft/archai

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LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models

https://github.com/microsoft/coco-lm

공식 코드

COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

https://github.com/microsoft/codet

공식 코드

RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation

https://github.com/microsoft/competeai

공식 코드

CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

https://github.com/microsoft/conli_hallucination

공식 코드

Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations

https://github.com/microsoft/constrainedreasoner

공식 코드

SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection

https://github.com/microsoft/dataflow2text

공식 코드

The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding

https://github.com/microsoft/deepspeedexamples

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DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention

https://github.com/microsoft/dki_llm

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RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning

https://github.com/microsoft/document-level-targeted-content-transfer

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Substance over Style: Document-Level Targeted Content Transfer

https://github.com/microsoft/dp-transformers

공식 코드

Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe

https://github.com/microsoft/efficientlongsequencemodeling

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Efficient Long Sequence Modeling via State Space Augmented Transformer

https://github.com/microsoft/geo-vision-labeler

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GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models

https://github.com/microsoft/glinthawk

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Glinthawk: A Two-Tiered Architecture for Offline LLM Inference

https://github.com/microsoft/godel

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GODEL: Large-Scale Pre-Training for Goal-Directed Dialog

https://github.com/microsoft/hallucinated-references

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Do Language Models Know When They're Hallucinating References?

https://github.com/microsoft/hi-ml/tree/main/hi-ml-multimodal

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Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing

https://github.com/microsoft/implicit_languagemodels

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Implicit Language Models are RNNs: Balancing Parallelization and Expressivity

https://github.com/microsoft/lavender

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LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling

https://github.com/microsoft/llm-rubric

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LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts

https://github.com/microsoft/lmops

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Optimizing Prompts for Text-to-Image Generation

https://github.com/microsoft/mars

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MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model

https://github.com/microsoft/muzic

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Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation

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