← 返回论文检索
KDD 2025Workshop Summaries

SKnow-LLM Workshop: Structured Knowledge for Large Language Models

Qi Zhu 0008, Xiusi Chen, Yu Zhang 0044, Soji Adeshina, Costas Mavromatis, Zhen Han, Vassilis N. Ioannidis, Leman Akoglu, Danai Koutra, Huzefa Rangwala

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.1145/3711896.3737845 ↗

摘要

Frontier large language models (LLMs) have demonstrated remarkable performance across various knowledge-intensive enterprise tasks. However, these models are primarily trained on unstructured, general knowledge, which limits their effectiveness in domain-specific applications-particularly when tasks involve structured data sources or sensitive enterprise information. We propose the first Structured Knowledge for Large Language Models Workshop - SKnow-LLM, which aims to bridge this gap by promoting research on innovative methodologies and practical applications in this area. Through keynote talks, panel discussions and paper presentations, the workshop will foster in-depth discussions on recent advances, identify existing challenges, and explore promising directions for integrating structured knowledge into LLMs.