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Language Modeling
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/daehuikim/WikiOFGraph
공식 코드https://github.com/dair-iitd/BossNet
공식 코드Disentangling Language and Knowledge in Task-Oriented Dialogs
https://github.com/dakingrai/ood-generalization-semantic-boundary-techniques
공식 코드https://github.com/damo-nlp-sg/contrastive-cot
공식 코드https://github.com/damo-nlp-sg/llm-data-annotator
공식 코드https://github.com/damo-nlp-sg/llm-r2
공식 코드LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency
https://github.com/damo-nlp-sg/pmr
공식 코드https://github.com/damo-nlp-sg/video-llama
공식 코드Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/dandanguo1993/vtcm-based-image-paragraph-caption
공식 코드Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning
https://github.com/dandoge/palm
공식 코드https://github.com/daniel-chin/flute-x-gpt
공식 코드https://github.com/daniel-gong/chatgpt-wm
공식 코드https://github.com/daniel442li/gpt-human-eval
공식 코드https://github.com/danielfperez/castillo
공식 코드CASTILLO: Characterizing Response Length Distributions of Large Language Models
https://github.com/danielsc4/dynamic-activation-composition
공식 코드Multi-property Steering of Large Language Models with Dynamic Activation Composition
https://github.com/danjohnvelasco/Filipino-ULMFiT
공식 코드Pagsusuri ng RNN-based Transfer Learning Technique sa Low-Resource Language
https://github.com/dao-ailab/flash-attention
공식 코드FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
https://github.com/dapascual/DirectedBeamSearch
공식 코드Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation
https://github.com/darl-libsignal/openti
공식 코드Open-TI: Open Traffic Intelligence with Augmented Language Model
https://github.com/daromog/q-vid
공식 코드Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering
https://github.com/darsh10/Nutribullets
공식 코드Nutri-bullets: Summarizing Health Studies by Composing Segments
https://github.com/darthreca/segmentation-losses-nlp
공식 코드Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning
https://github.com/datadrivenwheels/scvlm
공식 코드ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding
https://github.com/datngu/nli-artifacts
공식 코드https://github.com/davendw49/PK-Chat
공식 코드PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model
https://github.com/davendw49/k2
공식 코드K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
https://github.com/david-li0406/contextulization-distillation
공식 코드Contextualization Distillation from Large Language Model for Knowledge Graph Completion
https://github.com/davidandym/task-conflict-in-text-to-text-learners
공식 코드Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?
https://github.com/davidanugraha/proxylm
공식 코드ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models
https://github.com/davidbellamy/labrador
공식 코드Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
https://github.com/davidbrandfonbrener/color-filter-olmo
공식 코드CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training
https://github.com/davidecolla/semantic_coherence_markers
공식 코드Semantic Coherence Markers for the Early Diagnosis of the Alzheimer Disease
https://github.com/davidherel/sota_lm
공식 코드https://github.com/davidmchan/clair-a
공식 코드CLAIR-A: Leveraging Large Language Models to Judge Audio Captions
https://github.com/davidmrau/mixture-of-experts
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.