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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/gingasan/lemon
공식 코드Rethinking Masked Language Modeling for Chinese Spelling Correction
https://github.com/gingasan/mometas
공식 코드Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning
https://github.com/gingl/earn
공식 코드Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
https://github.com/ginnm/proteinpretraining
공식 코드https://github.com/giskard-ai/realharm
공식 코드RealHarm: A Collection of Real-World Language Model Application Failures
https://github.com/git-disl/awesome-llm-game-agent-papers
공식 코드https://github.com/git-disl/gptlens
공식 코드Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives
https://github.com/git-disl/safety-tax
공식 코드Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable
https://github.com/git-disl/vaccine
공식 코드Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
https://github.com/githubnext/testpilot
공식 코드An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
https://github.com/gitter-lab/Assay2Mol
공식 코드Assay2Mol: large language model-based drug design using BioAssay context
https://github.com/gitter-lab/chemlml
공식 코드Chemical Language Model Linker: blending text and molecules with modular adapters
https://github.com/gitycc/phonetic_mlm
공식 코드Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition
https://github.com/giventicket/cycleformer
공식 코드https://github.com/gjwubyron/icu
공식 코드https://github.com/gk0wk/xgraphrag
공식 코드XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
https://github.com/gkakogeorgiou/attmask
공식 코드What to Hide from Your Students: Attention-Guided Masked Image Modeling
https://github.com/gkw0010/endochat
공식 코드EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery
https://github.com/glaciohound/lm-steer
공식 코드https://github.com/glemhel/activationsgradientscompressionformp
공식 코드Activations and Gradients Compression for Model-Parallel Training
https://github.com/glnmario/cwr4lsc
공식 코드Analysing Lexical Semantic Change with Contextualised Word Representations
https://github.com/glowworm-cell/lict
공식 코드Objectively Evaluating the Reliability of Cell Type Annotation Using LLM-Based Strategies
https://github.com/gls0425/linvt
공식 코드LinVT: Empower Your Image-level Large Language Model to Understand Videos
https://github.com/gmaher/llmpc
공식 코드https://github.com/gmftbygmftby/copyisallyouneed
공식 코드https://github.com/gmlwns2000/sea-attention
공식 코드https://github.com/gmpoli/electramed
공식 코드ELECTRAMed: a new pre-trained language representation model for biomedical NLP
https://github.com/gmum/rsoftmax
공식 코드r-softmax: Generalized Softmax with Controllable Sparsity Rate
https://github.com/gnimixy/lancer
공식 코드https://github.com/godofhoe/gene-language-correspondence
공식 코드https://github.com/going-doer/judge_code_efficiency
공식 코드Rethinking Code Refinement: Learning to Judge Code Efficiency
https://github.com/gokamoda/TTA4FactualProbing
공식 코드https://github.com/gokulkarthik/deformable-detr
공식 코드An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers
https://github.com/gokulkarthik/detr
공식 코드An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers
https://github.com/goliaro/specinfer-ae
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.