让大模型更可信高效，助力数据库任务落地
Trustworthy and Efficient LLMs Meet Databases
- 从减少幻觉与提升推理效率两方面优化大模型输出
- 揭示大模型与数据库融合的协同机制与实际挑战
- 为数据库研究者提供大模型实用入门指南
在以大语言模型（LLMs）为核心的快速演进的AI时代，提升大模型的可信度与推理效率已成为关键课题，旨在降低看似合理却错误的生成内容（即幻觉），并应对激增的推理需求。本教程系统梳理了相关研究进展，向数据库领域研究者阐明这些技术的核心思路。理解这些方法对于在数据库任务中有效利用大模型、以及将数据库技术适配至大模型具有重要意义。同时，深入探讨大模型与数据库之间的协同潜力，揭示两者交叉领域的新兴机遇与挑战。本教程旨在帮助数据库研究人员与实践者掌握大模型的关键概念与策略，降低对大模型的技术陌生感，并激发其参与该交叉领域的热情。
原文摘要 · Abstract (English)
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.
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