KG LLM Papers

KG-LLM-Papers is a curated research compilation dedicated to the intersection of Knowledge Graphs and Large Language Models. This resource serves as a centralized repository for academic papers that explore techniques for integrating structured symbolic knowledge from graphs with the generative and reasoning capabilities of transformer-based models. The collection covers a wide range of methodologies including graph-enhanced pre-training, retrieval-augmented generation using graph contexts, knowledge-guided fine-tuning, and methods to improve factual accuracy and reduce hallucinations in LLM outputs. It also features studies on aligning unstructured text data with structured knowledge bases, as well as applications in domains requiring high precision such as healthcare, finance, and scientific discovery. Researchers and developers utilize this list to stay updated on the latest advancements, benchmark algorithms, and identify open challenges in merging these two powerful artificial intelligence paradigms. The