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Efficient Data Systems for Scalable Analysis With Large Language Models /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Efficient Data Systems for Scalable Analysis With Large Language Models /Saehan Jo.
作者:	Jo, Saehan,
面頁冊數:	1 electronic resource (224 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-12, Section: A.
附註:	Advisors: Trummer, Immanuel Committee members: Wagner, Aaron; Ellis, Kevin; Kuleshov, Volodymyr.
Contained By:	Dissertations Abstracts International86-12A.
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31997347
ISBN:	9798283140506

Efficient Data Systems for Scalable Analysis With Large Language Models /
Jo, Saehan,

Efficient Data Systems for Scalable Analysis With Large Language Models /Saehan Jo. - 1 electronic resource (224 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: A.

Modern data analysis increasingly relies on large language models (LLMs) to process diverse data modalities, including text, images, audio, and video. Although LLMs offer advanced reasoning capabilities for unstructured data, their high inference costs significantly exceed those of traditional relational operators. This computational overhead often becomes a critical bottleneck, limiting the scalability of LLM-driven analytical systems in real-world applications.This thesis introduces two complementary systems designed to mitigate this challenge and enable cost-efficient data analysis with LLMs. Both systems leverage principles from approximate query processing to balance computational cost and result quality. The first, ThalamusDB, reduces inference costs by minimizing the volume of data processed. Guided by specialized cost and error models, ThalamusDB identifies an optimal data subset for processing while respecting user-defined error constraints. The second system, SpareLLM, reduces costs by automatically selecting the smallest, most efficient LLM that satisfies user-defined equivalence constraints compared to a powerful reference model. Together, these systems present distinct strategies for achieving scalable data analysis with LLMs. Furthermore, this work introduces BitGourmet and AggChecker to address related challenges in specialized data processing and result verification, respectively.

English

ISBN: 9798283140506Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Large language models

Efficient Data Systems for Scalable Analysis With Large Language Models /
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