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Knowledge Graph Completion

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하위 태스크: 4
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: $(h, r, ?)$.

Source: One-Shot Relational Learning for Knowledge Graphs

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

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SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation

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Generating Origin-Destination Matrices in Neural Spatial Interaction Models

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CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion

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Challenges of Linking Organizational Information in Open Government Data to Knowledge Graphs

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Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling

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Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia

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Listen, Think, and Understand

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Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text

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Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams

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EQ-Negotiator: An Emotion-Reasoning LLM Agent in Credit Dialogues

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LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship Preservation

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Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

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Scene Graph Parsing as Dependency Parsing

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KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment

https://github.com/ZGChung/Mix-of-Granularity

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Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation

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L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?

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MotionLLaMA: A Unified Framework for Motion Synthesis and Comprehension

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Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding

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Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model

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Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization

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Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

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An Industry Evaluation of Embedding-based Entity Alignment

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500xCompressor: Generalized Prompt Compression for Large Language Models

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ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement

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Toward Conversational Agents with Context and Time Sensitive Long-term Memory

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COPA-SSE: Semi-structured Explanations for Commonsense Reasoning

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data

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Zero-shot generation of synthetic neurosurgical data with large language models

https://github.com/aabarr/synthetic-datasets

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Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generation

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Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs

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Discovering User-Interpretable Capabilities of Black-Box Planning Agents

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GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction Exploration

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Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients

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Punctuation Restoration Improves Structure Understanding Without Supervision

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