可信且高效的LLM与数据库的融合
Trustworthy and Efficient LLMs Meet Databases
AI总结：
本教程探讨如何使大型语言模型在推理过程中更可信高效，减少幻觉，并深入分析LLMs与数据库技术的协同作用及交叉领域的新机遇与挑战。
摘要：
在以大型语言模型（LLMs）为核心的快速发展的AI时代，使LLMs更加可信和高效，特别是在输出生成（推理）方面，已引起了广泛关注。这是为了减少看似合理但错误的LLM输出（即幻觉），并满足大幅增加的推理需求。本教程探讨了这些努力，并将其向数据库社区透明化。理解这些努力对于在数据库任务中利用LLMs以及将数据库技术适应于LLMs至关重要。此外，我们深入探讨了LLMs与数据库之间的协同作用，强调了它们交叉领域中的新机遇和挑战。本教程旨在与数据库研究人员和从业者分享关于LLMs的基本概念和策略，减少对LLMs的陌生感，并激发参与LLMs与数据库的交叉领域。
英文摘要：
In the rapidly evolving AI era with large language models (LLMs) at the core, making LLMs more trustworthy and efficient, especially in output generation (inference), has gained significant attention. This is to reduce plausible but faulty LLM outputs (a.k.a hallucinations) and meet the highly increased inference demands. This tutorial explores such efforts and makes them transparent to the database community. Understanding these efforts is essential in harnessing LLMs in database tasks and adapting database techniques to LLMs. Furthermore, we delve into the synergy between LLMs and databases, highlighting new opportunities and challenges in their intersection. This tutorial aims to share with database researchers and practitioners essential concepts and strategies around LLMs, reduce the unfamiliarity of LLMs, and inspire joining in the intersection between LLMs and databases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
