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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/arumaekawa/dilm
공식 코드DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
https://github.com/arvid-pku/atoke
공식 코드History Matters: Temporal Knowledge Editing in Large Language Model
https://github.com/aryan-sajith/urv-data_quantity_vs_data_quality-research
공식 코드Is Training Data Quality or Quantity More Impactful to Small Language Model Performance?
https://github.com/aryopg/vaxformer
공식 코드Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2
https://github.com/asFeng/Diffuser
공식 코드Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences
https://github.com/asad14053/AdCare-VLM
공식 코드https://github.com/asahi417/analogy-language-model
공식 코드BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?
https://github.com/asahi417/lm-question-generation
공식 코드An Empirical Comparison of LM-based Question and Answer Generation Methods
https://github.com/asahi417/lm-vocab-trimmer
공식 코드An Efficient Multilingual Language Model Compression through Vocabulary Trimming
https://github.com/asahi417/relbert
공식 코드Distilling Relation Embeddings from Pre-trained Language Models
https://github.com/asahi417/tner
공식 코드T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition
https://github.com/asappresearch/clip
공식 코드CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes
https://github.com/asappresearch/domain-private-transformers
공식 코드https://github.com/asappresearch/flop
공식 코드https://github.com/asappresearch/halugen
공식 코드https://github.com/asappresearch/josh-llm-simulation-training
공식 코드https://github.com/asappresearch/sru
공식 코드When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute
https://github.com/asayeed/activebaby
공식 코드https://github.com/asgaardlab/LLMxBugs
공식 코드Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors
https://github.com/asgsaeid/cad_vqa
공식 코드How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?
https://github.com/ash-ai-safety-hub/g3-nandi
공식 코드Comparing Optimization Targets for Contrast-Consistent Search
https://github.com/ashaba1in/smoothie
공식 코드Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation
https://github.com/ashfarhangi/Protoformer
공식 코드https://github.com/asiddhant/taskonomy-nlp
공식 코드Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks
https://github.com/assassinws/llm-tamp
공식 코드LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
https://github.com/astra-vision/prolip
공식 코드Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia
https://github.com/athms/learning-from-brains
공식 코드Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data
https://github.com/atla-ai/selene-mini
공식 코드https://github.com/atosystem/speechclip
공식 코드SpeechCLIP: Integrating Speech with Pre-Trained Vision and Language Model
https://github.com/atrewin/pgen
공식 코드Scaling Back-Translation with Domain Text Generation for Sign Language Gloss Translation
https://github.com/atten4vis/lw-detr
공식 코드OVLW-DETR: Open-Vocabulary Light-Weighted Detection Transformer
https://github.com/atulkum/pointer_summarizer
공식 코드https://github.com/aub-mind/araBERT
공식 코드AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding
https://github.com/audioku/meta-transfer-learning
공식 코드https://github.com/augustinLib/SPIKE
공식 코드Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.