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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/zihangdai/mos
공식 코드Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/zihangdai/xlnet
공식 코드XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/zihanwangki/fffner
공식 코드https://github.com/zihaow123/unimm
공식 코드Unified Multimodal Model with Unlikelihood Training for Visual Dialog
https://github.com/zimingyy/subzero
공식 코드https://github.com/zinengtang/VidLanKD
공식 코드VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer
https://github.com/zinengtang/perceiver_vl
공식 코드Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention
https://github.com/zipengxuc/ppe
공식 코드https://github.com/zirui-ray-liu/wtacrs
공식 코드Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
https://github.com/zivaharoni/gradual-learning-rnn
공식 코드https://github.com/zixuanke/pycontinual
공식 코드Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
https://github.com/ziyuguo99/point-bind_point-llm
공식 코드https://github.com/zjlab-ammi/llm4teach
공식 코드Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
https://github.com/zjr2000/llmva-gebc
공식 코드LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning
https://github.com/zju-vipa/odyssey
공식 코드Parallelized Planning-Acting for Efficient LLM-based Multi-Agent Systems
https://github.com/zjukeliu/s2snet
공식 코드S2SNet: A Pretrained Neural Network for Superconductivity Discovery
https://github.com/zjukg/conferenceqa
공식 코드Reliable Academic Conference Question Answering: A Study Based on Large Language Model
https://github.com/zjukg/kg-mm-survey
공식 코드Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/zjukg/kopa
공식 코드Making Large Language Models Perform Better in Knowledge Graph Completion
https://github.com/zjumai/evopatient
공식 코드LLMs Can Simulate Standardized Patients via Agent Coevolution
https://github.com/zjunlp/BiasEdit
공식 코드BiasEdit: Debiasing Stereotyped Language Models via Model Editing
https://github.com/zjunlp/DART
공식 코드Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
https://github.com/zjunlp/Deco
공식 코드MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
https://github.com/zjunlp/DeepKE/tree/main/example/llm
공식 코드CodeKGC: Code Language Model for Generative Knowledge Graph Construction
https://github.com/zjunlp/KnowPrompt
공식 코드KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
https://github.com/zjunlp/MolGen
공식 코드https://github.com/zjunlp/Prompt4ReasoningPapers
공식 코드https://github.com/zjunlp/PromptKG/tree/main/deltaKG
공식 코드https://github.com/zjunlp/PromptKG/tree/main/research/GenKGC
공식 코드From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
https://github.com/zjunlp/SemEval2021Task4
공식 코드https://github.com/zjunlp/deepke
공식 코드CodeKGC: Code Language Model for Generative Knowledge Graph Construction
https://github.com/zjunlp/easyedit
공식 코드EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models
https://github.com/zjunlp/knowledge-rumination
공식 코드https://github.com/zjunlp/knowledgecircuits
공식 코드https://github.com/zjunlp/knowlm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.