https://github.com/nibzard/agent-perceptions
공식 코드Will Agents Replace Us? Perceptions of Autonomous Multi-Agent AI
Papers with Code 아카이브
Knowledge Graph Completion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 7
- 보존된 평가 기록
- 17
Knowledge graphs $G$ are represented as a collection of triples $\\{(h, r, t)\\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nicerwang/joint-gcg
공식 코드Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems
https://github.com/nick-zheng-q/hdlxgraph
공식 코드HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databases
https://github.com/nicoladainese96/code-world-models
공식 코드Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
https://github.com/nicolas-hbt/benchmark-sematk
공식 코드Sem@$K$: Is my knowledge graph embedding model semantic-aware?
https://github.com/nicolas-hbt/pygraft
공식 코드PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
https://github.com/nicolas-hbt/semantic-lossfunc
공식 코드https://github.com/nicolas-hbt/versatile-embeddings
공식 코드Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
https://github.com/nicolay-r/semeval2024-task3
공식 코드https://github.com/nicolay-r/thor-ecac
공식 코드https://github.com/nicyscaria/AEQG-MCQ-Distractors-Physics
공식 코드https://github.com/nidryen-zh/privacyannotation
공식 코드Automated Privacy Information Annotation in Large Language Model Interactions
https://github.com/nidryen-zh/questionerawareresponder
공식 코드Personalized LLM for Generating Customized Responses to the Same Query from Different Users
https://github.com/nightdessert/retrieval_head
공식 코드Retrieval Head Mechanistically Explains Long-Context Factuality
https://github.com/nikivanstein/LLaMEA
공식 코드LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics
https://github.com/nikolamilosevic86/verif.ai
공식 코드https://github.com/nil-zhuang/efficientrag-official
공식 코드EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
https://github.com/ninecl/dekg-ilp
공식 코드Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction
https://github.com/ninibymilk/pmf-mmea
공식 코드Progressively Modality Freezing for Multi-Modal Entity Alignment
https://github.com/ninjatech-ai/mple
공식 코드Multi-Programming Language Ensemble for Code Generation in Large Language Model
https://github.com/niraj17singh/linklogic
공식 코드LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph Predictions
https://github.com/nishchalprasad/mesc
공식 코드https://github.com/niteshroyal/solvinghardanalogyquestions
공식 코드Solving Hard Analogy Questions with Relation Embedding Chains
https://github.com/niuchuangnn/vllm_structure
공식 코드https://github.com/niva-knowledge-graph/kgs_and_effect_prediction_2020
공식 코드Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
https://github.com/nju-pcalab/rag-diffusion
공식 코드Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement
https://github.com/nju-websoft/AliNet
공식 코드Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
https://github.com/nju-websoft/CR2
공식 코드Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
https://github.com/nju-websoft/DSKG
공식 코드DSKG: A Deep Sequential Model for Knowledge Graph Completion
https://github.com/nju-websoft/DeepLENS
공식 코드https://github.com/nju-websoft/HyperKA
공식 코드Knowledge Association with Hyperbolic Knowledge Graph Embeddings
https://github.com/nju-websoft/KG-ICL
공식 코드A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning
https://github.com/nju-websoft/KG2RAG
공식 코드https://github.com/nju-websoft/MultiKE
공식 코드https://github.com/nju-websoft/OpenEA
공식 코드A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
https://github.com/nju-websoft/RGRec
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.