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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/TRAILab/ST-SLidR
공식 코드Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss
https://github.com/TSKumarage/Neural-Authorship-Attribution
공식 코드Neural Authorship Attribution: Stylometric Analysis on Large Language Models
https://github.com/TSKumarage/Stylo-Det-AI-Gen-Twitter-Timelines
공식 코드Stylometric Detection of AI-Generated Text in Twitter Timelines
https://github.com/TUM-AVS/DualAD
공식 코드DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving
https://github.com/TableLLM/TableLLM
공식 코드TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
https://github.com/TangJiakai/GenSim
공식 코드GenSim: A General Social Simulation Platform with Large Language Model based Agents
https://github.com/Taniya-Das/video_annotation
공식 코드https://github.com/TartuNLP/xsim
공식 코드Cross-lingual Similarity of Multilingual Representations Revisited
https://github.com/TencentGameMate/gnn_cross_lingual
공식 코드Cross-lingual Text Classification with Heterogeneous Graph Neural Network
https://github.com/Tharun24/MACH
공식 코드https://github.com/TheOpenSI/CoSMIC
공식 코드Unleashing Artificial Cognition: Integrating Multiple AI Systems
https://github.com/Thesys-lab/Helix-ASPLOS25
공식 코드Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow
https://github.com/Thinksky5124/SVTAS
공식 코드https://github.com/ThomasWestfechtel/SWG
공식 코드https://github.com/TickleForce/ancient-greek-datasets
공식 코드Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation
https://github.com/TokisakiKurumi2001/UniBridge
공식 코드UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages
https://github.com/TongLiu-github/TemperatureSaling4RTs
공식 코드https://github.com/TonyLianLong/LLM-groundedDiffusion
공식 코드https://github.com/TonyNemo/UBAR-MultiWOZ
공식 코드UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2
https://github.com/TrumanPhd/f5C-finder
공식 코드https://github.com/TrustAIRLab/Conversation_Reconstruction_Attack
공식 코드https://github.com/TrustAIRLab/VoiceJailbreakAttack
공식 코드https://github.com/Trusted-AI/adversarial-robustness-toolbox
공식 코드Weakly Supervised Detection of Hallucinations in LLM Activations
https://github.com/TsinghuaAI/CPM-Generate
공식 코드CPM: A Large-scale Generative Chinese Pre-trained Language Model
https://github.com/TurkuNLP/bert-eval
공식 코드https://github.com/UBC-NLP/marbert
공식 코드ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
https://github.com/UBC-NLP/microdialects
공식 코드Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments
https://github.com/UCF-ML-Research/CR-UTP
공식 코드CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models
https://github.com/UFAL-DSG/cs_restaurant_dataset
공식 코드https://github.com/UFAL-DSG/tgen
공식 코드https://github.com/UKPLab/GritHopper
공식 코드https://github.com/UKPLab/arxiv2024-IRCoder
공식 코드IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators
https://github.com/UKPLab/controlled-argument-generation
공식 코드https://github.com/UKPLab/elmo-bilstm-cnn-crf
공식 코드https://github.com/UKPLab/sentence-transformers
공식 코드Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.