Papers with Code 아카이브
Large Language Model
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/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
https://github.com/nico-koltermann/contextual-evolution-of-heuristics
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
https://github.com/nicolas-hbt/versatile-embeddings
https://github.com/nineabyss/zerog
A Survey of Graph Meets Large Language Model: Progress and Future Directions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.