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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/google-deepmind/asyncdiloco
공식 코드https://github.com/google-deepmind/language_modeling_is_compression
공식 코드https://github.com/google-deepmind/librispeech-long
공식 코드https://github.com/google-deepmind/md4
공식 코드Simplified and Generalized Masked Diffusion for Discrete Data
https://github.com/google-deepmind/recurrentgemma
공식 코드RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
https://github.com/google-deepmind/reward-ensembles
공식 코드Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking
https://github.com/google-health/records-research
공식 코드Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes
https://github.com/google-research-datasets/KELM-corpus
공식 코드Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
https://github.com/google-research-datasets/ToTTo
공식 코드https://github.com/google-research-datasets/altentities
공식 코드Resolving Indirect Referring Expressions for Entity Selection
https://github.com/google-research-datasets/dakshina
공식 코드Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
https://github.com/google-research-datasets/gsm-ic
공식 코드Large Language Models Can Be Easily Distracted by Irrelevant Context
https://github.com/google-research-datasets/screen_annotation
공식 코드ScreenAI: A Vision-Language Model for UI and Infographics Understanding
https://github.com/google-research-datasets/screen_qa
공식 코드ScreenAI: A Vision-Language Model for UI and Infographics Understanding
https://github.com/google-research-datasets/swim-ir
공식 코드Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval
https://github.com/google-research-datasets/xsum_hallucination_annotations
공식 코드https://github.com/google-research/bert
공식 코드Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/google-research/big_vision
공식 코드UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes
https://github.com/google-research/bleurt
공식 코드https://github.com/google-research/cascades
공식 코드https://github.com/google-research/causallm_icl
공식 코드https://github.com/google-research/dataset_grouper
공식 코드Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
https://github.com/google-research/deduplicate-text-datasets
공식 코드https://github.com/google-research/electra
공식 코드ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/google-research/federated
공식 코드Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
https://github.com/google-research/flan
공식 코드https://github.com/google-research/google-research
공식 코드Toward Better Storylines with Sentence-Level Language Models
https://github.com/google-research/google-research/tree/master/cfq_pt_vs_sa
공식 코드Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures
https://github.com/google-research/google-research/tree/master/felix
공식 코드https://github.com/google-research/google-research/tree/master/perso_arabic_norm
공식 코드https://github.com/google-research/google-research/tree/master/primer
공식 코드Primer: Searching for Efficient Transformers for Language Modeling
https://github.com/google-research/google-research/tree/master/scalable_shampoo/jax
공식 코드https://github.com/google-research/google-research/tree/master/sm3
공식 코드https://github.com/google-research/google-research/tree/master/smith
공식 코드https://github.com/google-research/google-research/tree/master/structformer
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.