Language Modelling
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/graphformers
공식 코드GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
https://github.com/microsoft/hallucinated-references
공식 코드Do Language Models Know When They're Hallucinating References?
https://github.com/microsoft/hi-ml/tree/main/hi-ml-multimodal
공식 코드Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing
https://github.com/microsoft/implicit_languagemodels
공식 코드Implicit Language Models are RNNs: Balancing Parallelization and Expressivity
https://github.com/microsoft/lavender
공식 코드LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling
https://github.com/microsoft/llm-rubric
공식 코드https://github.com/microsoft/lmops
공식 코드https://github.com/microsoft/mars
공식 코드MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model
https://github.com/microsoft/mttl
공식 코드https://github.com/microsoft/muzic
공식 코드Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation
https://github.com/microsoft/muzic/tree/main/deeprapper
공식 코드DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling
https://github.com/microsoft/pengi
공식 코드https://github.com/microsoft/pike-rag
공식 코드PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
https://github.com/microsoft/promptpex
공식 코드PromptPex: Automatic Test Generation for Language Model Prompts
https://github.com/microsoft/pycodegpt
공식 코드https://github.com/microsoft/reina
공식 코드https://github.com/microsoft/rho
공식 코드https://github.com/microsoft/scglab
공식 코드https://github.com/microsoft/seed-encoder
공식 코드Less 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 코드BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing
https://github.com/microsoft/simxns
공식 코드https://github.com/microsoft/speecht5
공식 코드Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data
https://github.com/microsoft/stop
공식 코드Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
https://github.com/microsoft/synqo
공식 코드MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL
https://github.com/microsoft/torchscale
공식 코드https://github.com/microsoft/toxigen
공식 코드ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection
https://github.com/microsoft/uicaption
공식 코드https://github.com/microsoft/unilm
공식 코드Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/microsoft/unilm/blob/master/Diff-Transformer/
공식 코드https://github.com/microsoft/unilm/tree/master/LatentLM
공식 코드Multimodal Latent Language Modeling with Next-Token Diffusion
https://github.com/microsoft/unilm/tree/master/beit
공식 코드Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
https://github.com/microsoft/unilm/tree/master/kosmos-2
공식 코드Kosmos-2: Grounding Multimodal Large Language Models to the World
https://github.com/microsoft/unilm/tree/master/layoutlmv3
공식 코드LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/microsoft/unilm/tree/master/trocr
공식 코드TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://github.com/microsoft/unispeech
공식 코드Self-Supervised Learning for speech recognition with Intermediate layer supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.