OmniscientDB: a large language model-augmented DBMS that knows what other DBMSs do not know

  • WE PRESENT OUR VISION OF OMNISCIENTDB, A NOVEL DATABASE THAT LEVERAGES THE IMPLICITLY STORED KNOWLEDGE IN LARGE LANGUAGE MODELS TO AUGMENT DATA SETS FOR ANALYTICAL QUERIES OR MACHINE LEARNING TASKS. OMNISCIENTDB EMPOWERS USERS TO AUGMENT DATA SETS BY MEANS OF SIMPLE SQL QUERIES AND THUS HAS THE POTENTIAL TO DRAMATICALLY REDUCE THE MANUAL OVERHEAD ASSOCIATED WITH DATA INTEGRATION. IT USES AUTOMATIC PROMPT ENGINEERING TO CONSTRUCT APPROPRIATE PROMPTS FOR GIVEN SQL QUERIES AND PASSES THEM TO A LARGE LANGUAGE MODEL LIKE GPT-3 TO CONTRIBUTE ADDITIONAL DATA, AUGMENTING THE EXPLICITLY STORED DATA. OUR INITIAL EVALUATION DEMONSTRATES THE GENERAL FEASIBILITY OF OUR VISION, EXPLORES DIFFERENT PROMPTING TECHNIQUES IN GREATER DETAIL, AND POINTS TOWARDS FUTURE RESEARCH.

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Metadaten
Author:Matthias UrbanORCiDGND, Duc Dat NguyenORCiD, Carsten BinnigORCiDGND
URN:urn:nbn:de:hebis:30:3-744269
ISSN:1866-1238
ISSN:2700-2241
Parent Title (English):Efl insights : an elf - the Data Science Institute publication
Publisher:E-Finance Lab e.V.
Place of publication:Frankfurt am Main
Document Type:Article
Language:English
Date of Publication (online):2023/06/30
Date of first Publication:2023/06/30
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2023/07/03
Volume:2023
Issue:2
Page Number:3
First Page:6
Last Page:8
HeBIS-PPN:510051782
Institutes:Angeschlossene und kooperierende Institutionen / E-Finance Lab e.V.
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Sammlungen:Universit├Ątspublikationen
Licence (German):License LogoDeutsches Urheberrecht