From Large Language Models to Databases and Back: A Discussion on Research and Education

Sihem Amer-Yahia; Angela Bonifati; Lei Chen; Guoliang Li; Kyuseok Shim; Jianliang Xu; Xiaochun Yang

doi:10.1145/3631504.3631518

From Large Language Models to Databases and Back: A Discussion on Research and Education

Sihem Amer-Yahia, Angela Bonifati, Lei Chen, Guoliang Li, Kyuseok Shim, Jianliang Xu, Xiaochun Yang

Department of Computer Science

Research output: Contribution to journal › Journal article › peer-review

2 Citations (Scopus)

Abstract

In recent years, large language models (LLMs) have garnered increasing attention from both academia and industry due to their potential to facilitate natural language processing (NLP) and generate highquality text. Despite their benefits, however, the use of LLMs is raising concerns about the reliability of knowledge extraction. The combination of DB research and data science has advanced the state of the art in solving real-world problems, such as merchandise recommendation and hazard prevention [30]. In this discussion, we explore the challenges and opportunities related to LLMs in DB and data science research and education.

Original language	English
Pages (from-to)	49-56
Number of pages	8
Journal	SIGMOD Record
Volume	52
Issue number	3
DOIs	https://doi.org/10.1145/3631504.3631518
Publication status	Published - Sept 2023

Scopus Subject Areas

Software
Information Systems

Access to Document

10.1145/3631504.3631518

Cite this

@article{33dc5a0d04e243e8a8c2663a81cd2f16,

title = "From Large Language Models to Databases and Back: A Discussion on Research and Education",

abstract = "In recent years, large language models (LLMs) have garnered increasing attention from both academia and industry due to their potential to facilitate natural language processing (NLP) and generate highquality text. Despite their benefits, however, the use of LLMs is raising concerns about the reliability of knowledge extraction. The combination of DB research and data science has advanced the state of the art in solving real-world problems, such as merchandise recommendation and hazard prevention [30]. In this discussion, we explore the challenges and opportunities related to LLMs in DB and data science research and education.",

author = "Sihem Amer-Yahia and Angela Bonifati and Lei Chen and Guoliang Li and Kyuseok Shim and Jianliang Xu and Xiaochun Yang",

note = "Publisher Copyright: {\textcopyright} 2023 Copyright is held by the owner/author(s).",

year = "2023",

month = sep,

doi = "10.1145/3631504.3631518",

language = "English",

volume = "52",

pages = "49--56",

journal = "SIGMOD Record",

issn = "0163-5808",

publisher = "Association for Computing Machinery (ACM)",

number = "3",

}

TY - JOUR

T1 - From Large Language Models to Databases and Back

T2 - A Discussion on Research and Education

AU - Amer-Yahia, Sihem

AU - Bonifati, Angela

AU - Chen, Lei

AU - Li, Guoliang

AU - Shim, Kyuseok

AU - Xu, Jianliang

AU - Yang, Xiaochun

PY - 2023/9

Y1 - 2023/9

N2 - In recent years, large language models (LLMs) have garnered increasing attention from both academia and industry due to their potential to facilitate natural language processing (NLP) and generate highquality text. Despite their benefits, however, the use of LLMs is raising concerns about the reliability of knowledge extraction. The combination of DB research and data science has advanced the state of the art in solving real-world problems, such as merchandise recommendation and hazard prevention [30]. In this discussion, we explore the challenges and opportunities related to LLMs in DB and data science research and education.

AB - In recent years, large language models (LLMs) have garnered increasing attention from both academia and industry due to their potential to facilitate natural language processing (NLP) and generate highquality text. Despite their benefits, however, the use of LLMs is raising concerns about the reliability of knowledge extraction. The combination of DB research and data science has advanced the state of the art in solving real-world problems, such as merchandise recommendation and hazard prevention [30]. In this discussion, we explore the challenges and opportunities related to LLMs in DB and data science research and education.

UR - http://www.scopus.com/inward/record.url?scp=85176917082&partnerID=8YFLogxK

U2 - 10.1145/3631504.3631518

DO - 10.1145/3631504.3631518

M3 - Journal article

AN - SCOPUS:85176917082

SN - 0163-5808

VL - 52

SP - 49

EP - 56

JO - SIGMOD Record

JF - SIGMOD Record

IS - 3

ER -

From Large Language Models to Databases and Back: A Discussion on Research and Education

Abstract

Scopus Subject Areas

Access to Document

Other files and links

Fingerprint

Cite this