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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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Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models

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XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation

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14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

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Toward General Instruction-Following Alignment for Retrieval-Augmented Generation

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Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation

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HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions

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Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph

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Learning from Mistakes via Cooperative Study Assistant for Large Language Models

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TypedThinker: Typed Thinking Improves Large Language Model Reasoning

https://github.com/dqyzhwk/transkt

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Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer

https://github.com/dreamfold/foldflow

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Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation

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Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG

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Face the Facts! Evaluating RAG-based Fact-checking Pipelines in Realistic Settings

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Can Large Language Model Agents Balance Energy Systems?

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Assessing the Promise and Pitfalls of ChatGPT for Automated Code Generation

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Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB

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Speaker attribution in German parliamentary debates with QLoRA-adapted large language models

https://github.com/du-nlp-lab/lengthreward

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AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control

https://github.com/du-nlp-lab/lmr-bench

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LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research

https://github.com/du-nlp-lab/mlr-copilot

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MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents

https://github.com/duguodong7/graftllm

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Knowledge Grafting of Large Language Models

https://github.com/dunzeng/more

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On Diversified Preferences of Large Language Model Alignment

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM

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PclGPT: A Large Language Model for Patronizing and Condescending Language Detection

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Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks

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DropMicroFluidAgents (DMFAs): Autonomous Droplet Microfluidic Research Framework Through Large Language Model Agents

https://github.com/duyguislakoglu/temt

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Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs

https://github.com/dvlab-research/LLMGA

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LLMGA: Multimodal Large Language Model based Generation Assistant

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MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation

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LISA: Reasoning Segmentation via Large Language Model

https://github.com/dvlab-research/mr-gsm8k

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MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation

https://github.com/dwslab/jRDF2Vec

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RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings

https://github.com/dwzhu-pku/longembed

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LongEmbed: Extending Embedding Models for Long Context Retrieval

https://github.com/dxlong2000/multi-expert-prompting

공식 코드

Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models

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