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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/helsinki-nlp/lm-vs-mt
공식 코드A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives
https://github.com/hemile/iswc-challenge
공식 코드Prompting as Probing: Using Language Models for Knowledge Base Construction
https://github.com/hemingkx/speculativedecodingpapers
공식 코드https://github.com/henrypengzou/testnuc
공식 코드TestNUC: Enhancing Test-Time Computing Approaches through Neighboring Unlabeled Data Consistency
https://github.com/henryyantq/optimallm
공식 코드https://github.com/heraclex12/NLP2SPARQL
공식 코드https://github.com/hermansh-id/rcmha
공식 코드RCMHA: Relative Convolutional Multi-Head Attention for Natural Language Modelling
https://github.com/hertin/wavprompt
공식 코드WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models
https://github.com/hesfahanizadeh/neuralinformationshaping
공식 코드TexShape: Information Theoretic Sentence Embedding for Language Models
https://github.com/hetailang/squeezeattention
공식 코드SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget
https://github.com/hexuandeng/drpruning
공식 코드DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization
https://github.com/heyjoonkim/apa
공식 코드https://github.com/hfutami/distill-bert-for-seq2seq-asr
공식 코드Distilling the Knowledge of BERT for Sequence-to-Sequence ASR
https://github.com/hh23333/tsd
공식 코드Part Representation Learning with Teacher-Student Decoder for Occluded Person Re-identification
https://github.com/hhc1997/l2rm
공식 코드Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
https://github.com/hhexiy/pungen
공식 코드https://github.com/hhw-zhou/tsmmg
공식 코드Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model
https://github.com/hi-weiyuan/PTF-FSR
공식 코드PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System
https://github.com/hiagent2024/hiagent
공식 코드https://github.com/hiaoxui/soft-prompts
공식 코드Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
https://github.com/hieuphan33/mavl
공식 코드https://github.com/hieuthi/llamapartialspoof
공식 코드LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation
https://github.com/hijkzzz/pymarl2
공식 코드Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent Exploration
https://github.com/hillary-dawkins/genderswappedstereoset
공식 코드Projective Methods for Mitigating Gender Bias in Pre-trained Language Models
https://github.com/hillhao/PYNQ-project
공식 코드The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGA
https://github.com/hillzhang1999/gec_error_template
공식 코드https://github.com/hillzhang1999/mucgec
공식 코드https://github.com/him1411/edgar10q-dataset
공식 코드https://github.com/hirl-team/hirl
공식 코드HIRL: A General Framework for Hierarchical Image Representation Learning
https://github.com/hiroki11x/losslandscapegeometry
공식 코드No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
https://github.com/hiroto-takatoshi/ProgLMBug
공식 코드An Evalutation of Programming Language Models' performance on Software Defect Detection
https://github.com/histmeisah/large-language-models-play-starcraftii
공식 코드Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach
https://github.com/historicalink/LatamXIX
공식 코드Historical Ink: 19th Century Latin American Spanish Newspaper Corpus with LLM OCR Correction
https://github.com/historicalink/SSD-Old-Spanish
공식 코드Historical Ink: Semantic Shift Detection for 19th Century Spanish
https://github.com/hitachi-nlp/larch
공식 코드LARCH: Large Language Model-based Automatic Readme Creation with Heuristics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.