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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/dodoxxb/lamper
공식 코드LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
https://github.com/dom-s/comp-account-detect
공식 코드Semantic Text Analysis for Detection of Compromised Accounts on Social Networks
https://github.com/dominic789654/longgenbench
공식 코드https://github.com/dominiksinsaarland/DocSCAN
공식 코드DocSCAN: Unsupervised Text Classification via Learning from Neighbors
https://github.com/doncamilom/bollama
공식 코드https://github.com/dongguanting/msdp-fewshot-ner
공식 코드A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
https://github.com/dongyuanjushi/lightlm
공식 코드LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendation
https://github.com/donttal/tara
공식 코드https://github.com/doribear/A-Novel-Patent-Similarity-Measurement-Methodology
공식 코드A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance
https://github.com/dorin133/repe_alignment_helpfulness_tradeoff
공식 코드Tradeoffs Between Alignment and Helpfulness in Language Models with Representation Engineering
https://github.com/dosonleung/fasttog
공식 코드Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
https://github.com/douglasraevan/covid19-indonesian-misinformation-tweets
공식 코드Two-Stage Classifier for COVID-19 Misinformation Detection Using BERT: a Study on Indonesian Tweets
https://github.com/douglasraevan/covid19-indonesian-tweets
공식 코드Two-Stage Classifier for COVID-19 Misinformation Detection Using BERT: a Study on Indonesian Tweets
https://github.com/dpappas/Data-Augmentation-for-Biomedical-Factoid-Question-Answering
공식 코드https://github.com/dqwang122/eatlm
공식 코드https://github.com/dqwang122/salam
공식 코드Learning from Mistakes via Cooperative Study Assistant for Large Language Models
https://github.com/dqwang122/thinkhub
공식 코드TypedThinker: Typed Thinking Improves Large Language Model Reasoning
https://github.com/dr-costas/SEDLM
공식 코드Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling
https://github.com/dragnlabs/injectable-alignment-model
공식 코드https://github.com/dreamfold/foldflow
공식 코드Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
https://github.com/dreasysnail/POINTER
공식 코드POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training
https://github.com/dreasysnail/RetGen
공식 코드RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
https://github.com/dropreg/R-Drop
공식 코드https://github.com/dropreg/fennec
공식 코드https://github.com/drsleep/nas-segm-pytorch
공식 코드Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/drsy/easykv
공식 코드On the Efficacy of Eviction Policy for Key-Value Constrained Generative Language Model Inference
https://github.com/drsy/emo
공식 코드EMO: Earth Mover Distance Optimization for Auto-Regressive Language Modeling
https://github.com/drzekunguo/LLM_Agent_Power_System_Balance
공식 코드https://github.com/dsaatusu/chatgpt-promises-and-pitfalls
공식 코드Assessing the Promise and Pitfalls of ChatGPT for Automated Code Generation
https://github.com/dsba-lab/openal
공식 코드Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model
https://github.com/dsciitism/SemEval-2020-Task-6
공식 코드DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction
https://github.com/dsfsi/embedding-eval-data
공식 코드https://github.com/dsfsi/gov-za-multilingual
공식 코드Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora
https://github.com/dsfsi/puoberta
공식 코드PuoBERTa: Training and evaluation of a curated language model for Setswana
https://github.com/dsfsi/puodata
공식 코드PuoBERTa: Training and evaluation of a curated language model for Setswana
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.