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Language Modeling
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SkBlaz/attviz
공식 코드AttViz: Online exploration of self-attention for transparent neural language modeling
https://github.com/SkiddieAhn/Paper-AnyAnomaly
공식 코드AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
https://github.com/Smerity/sha-rnn
공식 코드https://github.com/Soumyabrata2003/ViLP
공식 코드ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action Recognition
https://github.com/Soumyadeepsar/Navigating-Nuance-In-Quest-for-Political-Truth
공식 코드https://github.com/SpeechClub/SLIDE-demo
공식 코드SLIDE: Integrating Speech Language Model with LLM for Spontaneous Spoken Dialogue Generation
https://github.com/Sri-Harsha/scpp
공식 코드SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations
https://github.com/StanfordAI4HI/ed-expert-simulator
공식 코드Evaluating and Optimizing Educational Content with Large Language Model Judgments
https://github.com/StarMoonWang/SeisMoLLM
공식 코드https://github.com/Stareru/NeighboringDistributionDivergence
공식 코드Contextualized Semantic Distance between Highly Overlapped Texts
https://github.com/StatNLP/ada4asr
공식 코드On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR
https://github.com/StonyBrookNLP/HAQAE
공식 코드Hierarchical Quantized Representations for Script Generation
https://github.com/SymbioticLab/FedScale
공식 코드FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
https://github.com/TAMU-VITA/BERT-Tickets
공식 코드https://github.com/TAP-LLM/SplitFedLLM
공식 코드Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
https://github.com/THU-BPM/SelfORE
공식 코드SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
https://github.com/THU-KEG/HIF-KAT
공식 코드Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making
https://github.com/THU-KEG/KEPLER
공식 코드KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
https://github.com/THUDM/CogAgent
공식 코드https://github.com/THUDM/GLM
공식 코드GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/THUDM/InversePrompting
공식 코드Controllable Generation from Pre-trained Language Models via Inverse Prompting
https://github.com/THUDM/OAG-BERT
공식 코드OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services
https://github.com/THUDM/P-tuning
공식 코드https://github.com/THUDM/ProteinLM
공식 코드https://github.com/TSKumarage/Neural-Authorship-Attribution
공식 코드Neural Authorship Attribution: Stylometric Analysis on Large Language Models
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/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/TokisakiKurumi2001/UniBridge
공식 코드UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages
https://github.com/TrumanPhd/f5C-finder
공식 코드https://github.com/Trusted-AI/adversarial-robustness-toolbox
공식 코드Weakly Supervised Detection of Hallucinations in LLM Activations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.