Lingua Manga:一个通用的大型语言模型中心的数据管理系统

IF 2.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611624

Zui Chen, Lei Cao, Sam Madden

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引用次数: 0

摘要

数据管理是一个广泛的领域，包含许多关键但耗时的数据处理任务。然而，这些任务的多样性使得开发通用的数据管理系统具有挑战性。为了解决这个问题，我们提出了Lingua Manga，这是一个用户友好且通用的系统，利用预训练的大型语言模型。Lingua Manga提供自动优化，以实现高性能和标签效率，同时促进灵活和快速的开发。通过三个具有不同目标和不同技术熟练程度用户的示例应用程序，我们证明了Lingua Manga可以有效地帮助熟练的程序员和低代码甚至无代码用户解决数据管理挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Lingua Manga : A Generic Large Language Model Centric System for Data Curation

Data curation is a wide-ranging area which contains many critical but time-consuming data processing tasks. However, the diversity of such tasks makes it challenging to develop a general-purpose data curation system. To address this issue, we present Lingua Manga, a user-friendly and versatile system that utilizes pre-trained large language models. Lingua Manga offers automatic optimization for achieving high performance and label efficiency while facilitating flexible and rapid development. Through three example applications with distinct objectives and users of varying levels of technical proficiency, we demonstrate that Lingua Manga can effectively assist both skilled programmers and low-code or even no-code users in addressing data curation challenges.

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来源期刊

Proceedings of the Vldb Endowment Computer Science-General Computer Science

CiteScore

7.70

自引率

0.00%

发文量

期刊介绍： The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.