Large Language Model
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zifengding/forecasttkgqa
공식 코드https://github.com/zifengding/zrllm
공식 코드zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
https://github.com/zihaowang/text_enhanced_kge
공식 코드Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces
https://github.com/zilin-huang/HAIM-DRL
공식 코드https://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/zjs123/anot
공식 코드Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
https://github.com/zju-daily/aistorian
공식 코드AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generation
https://github.com/zju-daily/dualmatch
공식 코드https://github.com/zju-vipa/odyssey
공식 코드https://github.com/zjukg/MEAformer
공식 코드MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
https://github.com/zjukg/adamf-mat
공식 코드Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
https://github.com/zjukg/ankge
공식 코드https://github.com/zjukg/conferenceqa
공식 코드Reliable Academic Conference Question Answering: A Study Based on Large Language Model
https://github.com/zjukg/earl
공식 코드Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
https://github.com/zjukg/kg-llm-papers
공식 코드https://github.com/zjukg/kg-mm-survey
공식 코드Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/zjukg/kgtransformer
공식 코드Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
https://github.com/zjukg/knowpat
공식 코드Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
https://github.com/zjukg/kopa
공식 코드Making Large Language Models Perform Better in Knowledge Graph Completion
https://github.com/zjukg/lpkg
공식 코드Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
https://github.com/zjukg/maco
공식 코드https://github.com/zjukg/maker
공식 코드Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
https://github.com/zjukg/momok
공식 코드https://github.com/zjukg/morse
공식 코드Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
https://github.com/zjukg/mygo
공식 코드Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
https://github.com/zjukg/neuralkg
공식 코드NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
https://github.com/zjukg/trustuqa
공식 코드TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
https://github.com/zjumai/evopatient
공식 코드LLMs Can Simulate Standardized Patients via Agent Coevolution
https://github.com/zjunlp/DeepKE/tree/main/example/llm
공식 코드How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?
https://github.com/zjunlp/Generative_KG_Construction_Papers
공식 코드https://github.com/zjunlp/MKG_Analogy
공식 코드https://github.com/zjunlp/MKGformer
공식 코드Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
https://github.com/zjunlp/autokg
공식 코드LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.