Research Trends for the Interplay between Large Language Models and Knowledge Graphs
원문 초록
This survey investigates the synergistic relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs), which is crucial for advancing AI's capabilities in understanding, reasoning, and language processing. It aims to address gaps in current research by exploring areas such as KG Question Answering, ontology generation, KG validation, and the enhancement of KG accuracy and consistency through LLMs. The paper further examines the roles of LLMs in generating descriptive texts and natural language queries for KGs. Through a structured analysis that includes categorizing LLM-KG interactions, examining methodologies, and investigating collaborative uses and potential biases, this study seeks to provide new insights into the combined potential of LLMs and KGs. It highlights the importance of their interaction for improving AI applications and outlines future research directions.
함께 살펴볼 논문
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Describe Anything Model for Visual Question Answering on Text-rich Images
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models
VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning
Towards Probabilistic Question Answering Over Tabular Data
Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs
토론
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