https://github.com/lmmapplication/rakg
공식 코드RAKG:Document-level Retrieval Augmented Knowledge Graph Construction
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RAKG:Document-level Retrieval Augmented Knowledge Graph Construction
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering
This Is Your Doge, If It Please You: Exploring Deception and Robustness in Mixture of LLMs
Representing Syntax and Composition with Geometric Transformations
Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
You Know What I'm Saying: Jailbreak Attack via Implicit Reference
CoVR-2: Automatic Data Construction for Composed Video Retrieval
Discovering Preference Optimization Algorithms with and for Large Language Models
Think Before You Act: Decision Transformers with Working Memory
InternVL-X: Advancing and Accelerating InternVL Series with Efficient Visual Token Compression
NePTuNe: Neural Powered Tucker Network for Knowledge Graph Completion
Code Soliloquies for Accurate Calculations in Large Language Models
Zero-Shot Relational Learning for Multimodal Knowledge Graphs
Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation
mDPO: Conditional Preference Optimization for Multimodal Large Language Models
Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis
PIVOINE: Instruction Tuning for Open-world Information Extraction
Multimodal Learning on Graphs for Disease Relation Extraction
DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics
VRKG4Rec: Virtual Relational Knowledge Graphs for Recommendation
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.