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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/JumpyPizza/align-sentence-simplification-with-ESL-learner
공식 코드Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition
https://github.com/Junction4Nako/mvp_pytorch
공식 코드MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage Learning
https://github.com/K-H-Ismail/torchortho
공식 코드https://github.com/KB-Ding/Multi-stage-Distillaton-Framework
공식 코드Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
https://github.com/KKbeckang/LiGPT-Beta
공식 코드https://github.com/KRR-Oxford/DeepOnto
공식 코드Contextual Semantic Embeddings for Ontology Subsumption Prediction
https://github.com/KRR-Oxford/OWL2Vec-Star
공식 코드https://github.com/KaiNylund/lm-weights-encode-time
공식 코드https://github.com/KaiserWhoLearns/Effective-Attention-Interpretability
공식 코드https://github.com/KaiyueSun98/T2V-CompBench
공식 코드T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation
https://github.com/Kaleidophon/deep-significance
공식 코드https://github.com/Kaleidophon/tenacious-toucan
공식 코드Recoding latent sentence representations -- Dynamic gradient-based activation modification in RNNs
https://github.com/KastanDay/video-pretrained-transformer
공식 코드Video Pre-trained Transformer: A Multimodal Mixture of Pre-trained Experts
https://github.com/Kaylee0501/X-MLClass
공식 코드Open-world Multi-label Text Classification with Extremely Weak Supervision
https://github.com/Kchu/LABOR-Agent
공식 코드https://github.com/Kevin-naticl/LLaSE-G1
공식 코드LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
https://github.com/KhoomeiK/complexity-scaling
공식 코드https://github.com/KhrulkovV/tt-pytorch
공식 코드https://github.com/KishoreP1/DetailCLIP
공식 코드https://github.com/KisuYang/EmotionX-KU
공식 코드https://github.com/Kodezi/Chronos
공식 코드https://github.com/KordingLab/llm4teach-freetext-server
공식 코드A large language model-assisted education tool to provide feedback on open-ended responses
https://github.com/KoubaPetr/Flexpert
공식 코드https://github.com/KurdishBLARK/HawramiClassification
공식 코드https://github.com/LAMDA-RL/CoLA
공식 코드https://github.com/LARC-CMU-SMU/RecipeGPT-exp
공식 코드RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System
https://github.com/LC1332/Chat-Haruhi-Suzumiya
공식 코드ChatHaruhi: Reviving Anime Character in Reality via Large Language Model
https://github.com/LCS2-IIITD/LESA-EACL-2021
공식 코드https://github.com/LIN-SHANG/InstructERC
공식 코드https://github.com/LINs-lab/M3
공식 코드https://github.com/LKJacky/Differentiable-Model-Scaling
공식 코드https://github.com/LL-a-VO/VLLaVO
공식 코드https://github.com/LLLQAQFFF/360REA
공식 코드https://github.com/LLebronC/TRECvid-VTT_HA
공식 코드BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment
https://github.com/LLer0829/Measuring_Copyright_Risk_of_LLM
공식 코드Measuring Copyright Risks of Large Language Model via Partial Information Probing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.