https://github.com/kingsaint/Wikidata-Descriptions
공식 코드Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts
Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 4
- 보존된 평가 기록
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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/kinianlo/contextuality-in-llm
공식 코드https://github.com/kitft/funsearch
공식 코드https://github.com/kjh9503/caqr
공식 코드https://github.com/kkskp/re-rag
공식 코드https://github.com/kkteru/grail
공식 코드https://github.com/kkyuhun94/dalda
공식 코드DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling
https://github.com/klarna/product-page-dataset
공식 코드https://github.com/kmatton/walk-the-talk
공식 코드Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations
https://github.com/kmswin1/comdense
공식 코드https://github.com/kmswin1/qpaug
공식 코드QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs
https://github.com/knowledge-graphs-tutorial/examples
공식 코드https://github.com/knowledge-ukraine/ontochatgpt
공식 코드OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-Learning
https://github.com/knowsys/2022-iswc-reliances
공식 코드https://github.com/knowsys/nemo
공식 코드https://github.com/kolejnyy/ANYCQ
공식 코드https://github.com/komeijiforce/emojilm
공식 코드https://github.com/kortukov/realistic_knowledge_conflicts
공식 코드Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents
https://github.com/kracr/rcone-qa-mmkg
공식 코드RConE: Rough Cone Embedding for Multi-Hop Logical Query Answering on Multi-Modal Knowledge Graphs
https://github.com/krafton-ai/rare-to-frequent
공식 코드https://github.com/kramerlab/focusedretriever
공식 코드Focus, Merge, Rank: Improved Question Answering Based on Semi-structured Knowledge Bases
https://github.com/krandiash/octopus
공식 코드Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing
https://github.com/krishgoel/chronocept-baseline-models
공식 코드https://github.com/krishgoel/hiro
공식 코드https://github.com/krr-oxford/boxsquaredel
공식 코드https://github.com/krr-oxford/oet
공식 코드Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
https://github.com/krystalan/RAGtrans
공식 코드Retrieval-Augmented Machine Translation with Unstructured Knowledge
https://github.com/krzentner/language-world
공식 코드Conditionally Combining Robot Skills using Large Language Models
https://github.com/kse-eleven/makged
공식 코드https://github.com/ktlktl/c-llm
공식 코드C-LLM: Learn to Check Chinese Spelling Errors Character by Character
https://github.com/ktr0921/tac
공식 코드A Minimal Approach for Natural Language Action Space in Text-based Games
https://github.com/kuanchihhuang/reason3d
공식 코드Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model
https://github.com/kuanglab/LowrankTLP
공식 코드Scalable Label Propagation for Multi-relational Learning on the Tensor Product of Graphs
https://github.com/kuehnram/findyourfigure
공식 코드Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration
https://github.com/kumarmanas/SHIFT
공식 코드Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification
https://github.com/kushagrabhushan/Systematic-Knowledge-Injection
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.