Reliable Large Models with Knowledge Augmentation
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Beschreibung
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have emerged as transformative tools, yet they face persistent challenges such as hallucination, knowledge obsolescence, and limited reasoning depth. The book "Knowledge-Enhanced Large Models" addresses these gaps head-on, offering a comprehensive roadmap for integrating structured knowledge systems—particularly knowledge graphs—with cutting-edge AI models to build more reliable, accurate, and context-aware intelligent systems. This book is tailored for AI researchers, data scientists, engineers, students, and practitioners seeking to harness the synergy between large models and knowledge representation technologies. It balances theoretical rigor with practical implementation, making it equally valuable for academic exploration and industrial application.
The book is organized into 10 chapters, systematically guiding readers from foundational concepts to advanced techniques and real-world applications. The first two chapters explore the rise of LLMs, their inherent limitations, and the paradigm shift toward knowledge-enhanced models. Chapters 3 to 5 delve into the infrastructure required to augment LLMs with structured knowledge. Chapters 6 to 9 explore cutting-edge methodologies for bridging symbolic knowledge systems with neural networks. The final chapter translates theory into practice, offering actionable guidelines for deploying knowledge-enhanced models across industries.
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have emerged as transformative tools, yet they face persistent challenges such as hallucination, knowledge obsolescence, and limited reasoning depth. The book "Knowledge-Enhanced Large Models" addresses these gaps head-on, offering a comprehensive roadmap for integrating structured knowledge systems—particularly knowledge graphs—with cutting-edge AI models to build more reliable, accurate, and context-aware intelligent systems. This book is tailored for AI researchers, data scientists, engineers, students, and practitioners seeking to harness the synergy between large models and knowledge representation technologies. It balances theoretical rigor with practical implementation, making it equally valuable for academic exploration and industrial application.
The book is organized into 10 chapters, systematically guiding readers from foundational concepts to advanced techniques and real-world applications. The first two chapters explore the rise of LLMs, their inherent limitations, and the paradigm shift toward knowledge-enhanced models. Chapters 3 to 5 delve into the infrastructure required to augment LLMs with structured knowledge. Chapters 6 to 9 explore cutting-edge methodologies for bridging symbolic knowledge systems with neural networks. The final chapter translates theory into practice, offering actionable guidelines for deploying knowledge-enhanced models across industries.
Combines LLM and knowledge graphs and offers a unified framework for building robust AI systems Step-by-step tutorials of tools such as Milvus, JanusGraph, and Dify, helping readers to operationalize concepts Blends techniques from NLP, graph theory, database and machine learning, providing readers with the latest applications
Autor*in
Wenguang Wang
Themen in »Reliable Large Models with Knowledge Augmentation«
Large Language Models Knowledge-Enhanced Models Knowledge Graphs Vector Databases Retrieval-Augmented Generation (RAG) Graph Databases GraphRAG Graph-Model Synergy Paradigm