https://github.com/JeongHun0716/MMS-LLaMA
공식 코드MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech Tokens
Papers with Code 아카이브
Language Modeling
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JesseTNRoberts/Large-Language-Model-Recall-Uncertainty-is-Modulated-by-the-Fan-Effect
공식 코드Large Language Model Recall Uncertainty is Modulated by the Fan Effect
https://github.com/JetRunner/PABEE
공식 코드BERT Loses Patience: Fast and Robust Inference with Early Exit
https://github.com/Jiang-Yidi/TransformerDistillation-SLU
공식 코드Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification
https://github.com/Jimin9401/avocado
공식 코드AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
https://github.com/JindongGu/Awesome-Prompting-on-Vision-Language-Model
공식 코드A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
https://github.com/JingzheShi/NLPCtlScalingAndBounds
공식 코드Explaining Context Length Scaling and Bounds for Language Models
https://github.com/Jirl-upenn/VLMnav
공식 코드https://github.com/Joe-Hall-Lee/Debias
공식 코드https://github.com/Joe-Hall-Lee/PSN-IRT
공식 코드Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theory
https://github.com/JoeRoussy/adaptive-attention-in-cv
공식 코드https://github.com/JonnoB/scrambledtext
공식 코드Scrambled text: training Language Models to correct OCR errors using synthetic data
https://github.com/JonnoB/scrambledtext_analysis
공식 코드Scrambled text: training Language Models to correct OCR errors using synthetic data
https://github.com/JonnoB/training_lms_with_synthetic_data
공식 코드Scrambled text: training Language Models to correct OCR errors using synthetic data
https://github.com/JoyeBright/ICLviaQE
공식 코드Guiding In-Context Learning of LLMs through Quality Estimation for Machine Translation
https://github.com/Jumponthemoon/WeatherDG
공식 코드https://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/KKbeckang/LiGPT-Beta
공식 코드https://github.com/KRR-Oxford/DeepOnto
공식 코드Contextual Semantic Embeddings for Ontology Subsumption Prediction
https://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/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/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 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.