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Knowledge Graph Completion

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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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LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input Contexts

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Beyond Text: Frozen Large Language Models in Visual Signal Comprehension

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FineRec:Exploring Fine-grained Sequential Recommendation

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Instruction Backdoor Attacks Against Customized LLMs

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Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics

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TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-On

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Enhancing Image Generation Fidelity via Progressive Prompts

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GEMA-Score: Granular Explainable Multi-Agent Score for Radiology Report Evaluation

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InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint

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Large Language Model with Region-guided Referring and Grounding for CT Report Generation

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A Lightweight Constrained Generation Alternative for Query-focused Summarization

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Context-aware Decoding Reduces Hallucination in Query-focused Summarization

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Reinforced Large Language Model is a formal theorem prover

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Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V

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HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion

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Anchoring Path for Inductive Relation Prediction in Knowledge Graphs

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Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer

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Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System

https://github.com/zhiyuanpeng/eloq

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ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions

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