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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/daniel442li/gpt-human-eval
공식 코드https://github.com/danielfperez/castillo
공식 코드CASTILLO: Characterizing Response Length Distributions of Large Language Models
https://github.com/danieljunior/jidm
공식 코드https://github.com/danielsc4/dynamic-activation-composition
공식 코드Multi-property Steering of Large Language Models with Dynamic Activation Composition
https://github.com/danjohnvelasco/Filipino-ULMFiT
공식 코드Pagsusuri ng RNN-based Transfer Learning Technique sa Low-Resource Language
https://github.com/danlou/LMMS
공식 코드https://github.com/danlou/bert-disambiguation
공식 코드Analysis and Evaluation of Language Models for Word Sense Disambiguation
https://github.com/danrsc/bert_brain_neurips_2019
공식 코드Inducing brain-relevant bias in natural language processing models
https://github.com/dao-ailab/flash-attention
공식 코드FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
https://github.com/dapascual/DirectedBeamSearch
공식 코드Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation
https://github.com/darl-libsignal/openti
공식 코드Open-TI: Open Traffic Intelligence with Augmented Language Model
https://github.com/daromog/q-vid
공식 코드Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering
https://github.com/darsh10/Nutribullets
공식 코드Nutri-bullets: Summarizing Health Studies by Composing Segments
https://github.com/darthreca/segmentation-losses-nlp
공식 코드Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning
https://github.com/datadrivenwheels/scvlm
공식 코드ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding
https://github.com/datasig-ac-uk/nlpsig
공식 코드Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling
https://github.com/datngu/nli-artifacts
공식 코드https://github.com/davendw49/PK-Chat
공식 코드PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model
https://github.com/davendw49/k2
공식 코드K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
https://github.com/david-li0406/contextulization-distillation
공식 코드Contextualization Distillation from Large Language Model for Knowledge Graph Completion
https://github.com/davidandym/task-conflict-in-text-to-text-learners
공식 코드Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?
https://github.com/davidanugraha/proxylm
공식 코드ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models
https://github.com/davidbellamy/labrador
공식 코드Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
https://github.com/davidbrandfonbrener/color-filter-olmo
공식 코드CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training
https://github.com/davidecolla/semantic_coherence_markers
공식 코드Semantic Coherence Markers for the Early Diagnosis of the Alzheimer Disease
https://github.com/davidheineman/category-fluency
공식 코드https://github.com/davidherel/sota_lm
공식 코드https://github.com/davidhuji/capdec
공식 코드Text-Only Training for Image Captioning using Noise-Injected CLIP
https://github.com/davidmchan/clair-a
공식 코드CLAIR-A: Leveraging Large Language Models to Judge Audio Captions
https://github.com/davidmrau/mixture-of-experts
공식 코드https://github.com/davidschulte/hf-dataset-selector
공식 코드Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning
https://github.com/davidsroth/hubert-disfl
공식 코드End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model Pretraining
https://github.com/davisrbr/bsd-misuse
공식 코드https://github.com/dawenl/vae_cf
공식 코드https://github.com/dayihengliu/Mu-Forcing-VRAE
공식 코드mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.