Will LLMs Reshape, Supercharge, or Kill Data Science? (VLDB 2023 Panel)

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:10.14778/3611540.3611634

Alon Halevy, Yejin Choi, Avrilia Floratou, Michael J. Franklin, Natasha Noy, Haixun Wang

引用次数: 1

Abstract

Large language models (LLMs) have recently taken the world by storm, promising potentially game changing opportunities in multiple fields. Naturally, there is significant promise in applying LLMs to the management of structured data, or more generally, to the processes involved in data science. At the very least, LLMs have the potential to provide substantial advancements in long-standing challenges that our community has been tackling for decades. On the other hand, they may introduce completely new capabilities that we have only dreamed of thus far. This panel will bring together a few leading experts who have been thinking about these opportunities from various perspectives and fielding them in research prototypes and even in commercial applications.

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法学硕士将重塑、强化还是扼杀数据科学?(VLDB 2023面板)

大型语言模型(llm)最近风靡全球，在多个领域提供了潜在的改变游戏规则的机会。当然，将法学硕士应用于结构化数据的管理，或者更一般地说，应用于数据科学中涉及的过程，有很大的前景。至少，法学硕士有潜力为我们的社区几十年来一直在解决的长期挑战提供实质性的进步。另一方面，它们可能会引入我们迄今为止只能梦想的全新功能。该小组将汇集一些领先的专家，他们一直在从不同的角度思考这些机会，并将其应用于研究原型甚至商业应用中。

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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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.