探索大模型与知识库融合，提升语言理解与知识精度。
A Comprehensive Survey on Integrating Large Language Models with Knowledge-Based Methods
- 将大模型生成能力与结构化知识系统结合，实现互补优势。
- 实证显示融合可提升数据上下文理解与模型准确率。
- 适合关注AI落地、知识增强系统的研发人员参考。
人工智能的快速发展推动了诸多进展。一个值得关注的研究方向是大型语言模型（LLMs）能否与结构化知识库系统相结合。该方法旨在融合大模型的生成式语言理解能力与知识库的精确知识表示能力。本文综述了大模型与知识库之间的关系，探讨其实际应用路径，并分析相关技术、运营与伦理挑战。通过全面文献考察，研究识别出关键问题并评估现有解决方案。结果表明，将生成式AI融入结构化知识库系统在数据上下文化、模型准确性及知识资源利用方面具有显著优势。研究梳理了当前研究现状，指出了主要差距，并提出了可行的发展路径。这些发现有助于推进AI技术进步，并支持其在各领域的实际部署。
原文摘要 · Abstract (English)
The rapid development of artificial intelligence has led to marked progress in the field. One interesting direction for research is whether Large Language Models (LLMs) can be integrated with structured knowledge-based systems. This approach aims to combine the generative language understanding of LLMs and the precise knowledge representation systems by which they are integrated. This article surveys the relationship between LLMs and knowledge bases, looks at how they can be applied in practice, and discusses related technical, operational, and ethical challenges. Utilizing a comprehensive examination of the literature, the study both identifies important issues and assesses existing solutions. It demonstrates the merits of incorporating generative AI into structured knowledge-base systems concerning data contextualization, model accuracy, and utilization of knowledge resources. The findings give a full list of the current situation of research, point out the main gaps, and propose helpful paths to take. These insights contribute to advancing AI technologies and support their practical deployment across various sectors.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。