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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/iqvianlp/llm-onto-infuse
공식 코드Towards Ontology-Enhanced Representation Learning for Large Language Models
https://github.com/ir-nlp-csui/indonli
공식 코드IndoNLI: A Natural Language Inference Dataset for Indonesian
https://github.com/ireinig/wogli
공식 코드https://github.com/irenamal/ec-kity
공식 코드https://github.com/irgroup/suir
공식 코드Context-Driven Interactive Query Simulations Based on Generative Large Language Models
https://github.com/iriscxy/fes
공식 코드https://github.com/irugina/AP
공식 코드Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
https://github.com/irugina1/llama-attention-pruning
공식 코드Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
https://github.com/is5882/uu-tax
공식 코드https://github.com/isabellelorge/dtd
공식 코드https://github.com/isarth/SemEval9_MIDAS
공식 코드https://github.com/ischlag/Fast-Weight-Memory-public
공식 코드https://github.com/ischlag/fast-weight-transformers
공식 코드https://github.com/ise-research/girt-model
공식 코드https://github.com/isee-laboratory/llmdet
공식 코드https://github.com/isen-zhang/aclue
공식 코드Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE
https://github.com/ishan00/translation-for-code-switching-acl
공식 코드From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text
https://github.com/ishikasingh/Affective-text-gen
공식 코드Adapting a Language Model for Controlled Affective Text Generation
https://github.com/ishutov/nlp2018_hp_asoif_rus
공식 코드https://github.com/isitnavajo/NavajoDetector
공식 코드Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languages
https://github.com/isl-mt/SemanticWordReplacement-LREC2018
공식 코드https://github.com/ismoptgroup/ramda
공식 코드https://github.com/ismoptgroup/ramda_exp
공식 코드https://github.com/issararab/pevolm
공식 코드PEvoLM: Protein Sequence Evolutionary Information Language Model
https://github.com/isspek/FakeNewsDetectionFramework
공식 코드ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information
https://github.com/ist-daslab/cram
공식 코드https://github.com/ist-daslab/gptq
공식 코드GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
https://github.com/ist-daslab/sparsegpt
공식 코드SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
https://github.com/istarryn/dllm2rec
공식 코드https://github.com/itay1itzhak/spellingbee
공식 코드Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens
https://github.com/itsdaniele/speculative_mamba
공식 코드The Mamba in the Llama: Distilling and Accelerating Hybrid Models
https://github.com/itsnamgyu/block-transformer
공식 코드Block Transformer: Global-to-Local Language Modeling for Fast Inference
https://github.com/iurteaga/gp_ts_nlp
공식 코드https://github.com/ivanmontero/autobot
공식 코드Sentence Bottleneck Autoencoders from Transformer Language Models
https://github.com/ivnle/synth-icl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.