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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/nxh912/BTRec_RecSys23
공식 코드BTRec: BERT-Based Trajectory Recommendation for Personalized Tours
https://github.com/nxphi47/multiagent_crosstranslate
공식 코드Cross-model Back-translated Distillation for Unsupervised Machine Translation
https://github.com/nyonicai/nyonic-public
공식 코드https://github.com/nyu-dl/NLP_DL_Lecture_Note
공식 코드Natural Language Understanding with Distributed Representation
https://github.com/nyu-dl/bert-gen
공식 코드BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
https://github.com/nyu-dl/gated_word_char_rlm
공식 코드https://github.com/nyu-dl/non-monotonic-self-terminating-lm
공식 코드https://github.com/nyu-mll/PRPN-Analysis
공식 코드Grammar Induction with Neural Language Models: An Unusual Replication
https://github.com/nyu-mll/wsc-formalizations
공식 코드Precise Task Formalization Matters in Winograd Schema Evaluations
https://github.com/nyunai/sfsd-llm
공식 코드Surgical Feature-Space Decomposition of LLMs: Why, When and How?
https://github.com/nyuolab/MedMobile
공식 코드MedMobile: A mobile-sized language model with expert-level clinical capabilities
https://github.com/nyuolab/model_sensitivity
공식 코드https://github.com/nyushcs/moleculargpt
공식 코드MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
https://github.com/nyushcs/uniglm
공식 코드UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding
https://github.com/o-vigia/gma3d
공식 코드GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow
https://github.com/oaishi/3DScene_from_text
공식 코드Static and Animated 3D Scene Generation from Free-form Text Descriptions
https://github.com/oaklight/mango
공식 코드MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models
https://github.com/oaraque/moral-foundations
공식 코드MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction
https://github.com/oashua/mathagent
공식 코드Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent
https://github.com/oddqueue/e-dpo
공식 코드KL Penalty Control via Perturbation for Direct Preference Optimization
https://github.com/odedh/textual-pca
공식 코드https://github.com/ofa-sys/expertllama
공식 코드ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
https://github.com/ofa-sys/ofa
공식 코드https://github.com/ofirarviv/ud-based-word-reordering
공식 코드Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering
https://github.com/ofirpress/PartialShuffle
공식 코드Partially Shuffling the Training Data to Improve Language Models
https://github.com/ofirpress/shortformer
공식 코드https://github.com/ogaloglu/pre-training-multilingual-document-encoders
공식 코드https://github.com/ohadrubin/epr
공식 코드https://github.com/ohadrubin/rpt
공식 코드Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval
https://github.com/okuchaiev/f-lm
공식 코드https://github.com/ola-omni/ola
공식 코드https://github.com/olivialyt/clip-pcqa
공식 코드CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality Assessment
https://github.com/om-ai-lab/rs5m
공식 코드https://github.com/om-ai-lab/vlm-r1
공식 코드VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
https://github.com/omar-sharif03/NAACL-SRW-2021
공식 코드Emotion Classification in a Resource Constrained Language Using Transformer-based Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.