# Trustworthy and Efficient LLMs Meet Databases

**Authors:** Kyoungmin Kim, Anastasia Ailamaki  
**arXiv:** [2412.18022](https://arxiv.org/abs/2412.18022)  
**Date:** 2024-12-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2412.18022v1/

## Key points

- Language models (LLMs) are central to various applications and play a crucial role in output generation or inference.
- Minimizing erroneous outputs, known as hallucinations, is a critical focus area for enhancing the reliability and performance of LLMs.
- Database practitioners can benefit from understanding and leveraging advancements in LLMs to integrate them into workflows effectively.
- There are synergies between LLMs and databases that present new opportunities for collaboration and innovation at their intersection.
- The tutorial aims to equip researchers and practitioners with essential insights and strategies for combining LLMs with database technologies.

## Summary

In the rapidly evolving ,  have become central to various applications, including  Ensuring the , particularly in output generation or inference, has emerged as a critical focus area. The goal is to minimize the occurrence of plausible yet erroneous outputs from LLMs, commonly referred to as hallucinations, while also meeting the escalating demands for accurate and reliable inferences. This tutorial delves into the ongoing efforts aimed at enhancing the reliability and performance of LLMs, shedding light on these initiatives for the benefit of the database community. By understanding and leveraging these advancements, database practitioners can effectively integrate LLMs into their workflows and adapt existing techniques to leverage the capabilities of these powerful language models. Furthermore, this tutorial explores the synergies between LLMs and databases, uncovering new opportunities for collaboration and innovation at their intersection. By highlighting both the potential benefits and challenges that arise when combining LLMs with database technologies, this tutorial aims to equip researchers and practitioners in the field with essential insights and strategies. Authors Kyoungmin Kim and Anastasia Ailamaki provide a comprehensive overview of essential concepts surrounding LLMs, aiming to demystify these complex models and encourage greater engagement in exploring the intersection between LLMs and databases. Through this exploration, they seek to inspire collaboration and knowledge-sharing among stakeholders from both domains, ultimately driving advancements in AI-driven database applications.