Can Foundation Models Wrangle Your Data?

نویسندگان

چکیده

Foundation Models (FMs) are models trained on large corpora of data that, at very scale, can generalize to new tasks without any task-specific finetuning. As these continue grow in size, innovations push the boundaries what do language and image tasks. This paper aims understand an underexplored area FMs: classical like cleaning integration. a proof-of-concept, we cast five integration as prompting evaluate performance FMs We find that achieve SoTA tasks, even though they not for identify specific research challenges opportunities present, including with private domain data, make management systems more accessible non-experts. our code experiments publicly available at: https://github.com/HazyResearch/fm_data_tasks.

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ژورنال

عنوان ژورنال: Proceedings of the VLDB Endowment

سال: 2022

ISSN: ['2150-8097']

DOI: https://doi.org/10.14778/3574245.3574258