Papers with Code 아카이브
Knowledge Graphs
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- 평가표
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A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/HittER
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
https://github.com/microsoft/LLMLingua
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention
https://github.com/microsoft/data-formulator
Data Formulator: AI-powered Concept-driven Visualization Authoring
https://github.com/microsoft/graphrag
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/microsoft/kear
Fusing Context Into Knowledge Graph for Commonsense Question Answering
https://github.com/microsoft/mttl
Towards Modular LLMs by Building and Reusing a Library of LoRAs
https://github.com/microsoft/rd-agent
https://github.com/microsoft/verus-proof-synthesis
https://github.com/migalkin/NodePiece/tree/main/ogb
https://github.com/miguelgondu/finding_game_levels_paper
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
https://github.com/mindspore-ai/models/tree/master/research/nlp/rotate
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/mindspore-courses/d2l-mindspore/tree/master/chapter_15_natural_language_processing_applications
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/mingdali6717/ensemble-of-retrievers
Generate rather than Retrieve: Large Language Models are Strong Context Generators
https://github.com/mit-han-lab/deepcompressor
QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
https://github.com/mit-han-lab/lmquant
QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
https://github.com/mlwin-de/MDE
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs
https://github.com/mlwin-de/MDE_adv
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs
https://github.com/mnick/holographic-embeddings
https://github.com/mnick/scikit-kge
https://github.com/mniepert/mmkb
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
https://github.com/modelscope/modelscope
https://github.com/modeltc/lightllm
FastSwitch: Optimizing Context Switching Efficiency in Fairness-aware Large Language Model Serving
https://github.com/muennighoff/sgpt
https://github.com/muhanzhang/IGMC
https://github.com/muhanzhang/IGPL
https://github.com/muhaochen/MTransE
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
https://github.com/muhaochen/MTransE-tf
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
https://github.com/mzhaoshuai/slimclr
Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training
https://github.com/nalexai/hyperlib
https://github.com/narabzad/llm-relevance-judgement-comparison
Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the AutoNuggetizer Framework
https://github.com/naver-ai/jood
https://github.com/nec-research/dccg
https://github.com/ngl567/RPJE
Rule-Guided Compositional Representation Learning on Knowledge Graphs
https://github.com/nguyentthong/temporal_recipe
Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video Understanding
https://github.com/nickm980/smallville
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.