TY  - JOUR
A1  - Urban, Matthias
A1  - Nguyen, Duc Dat
A1  - Binnig, Carsten
T1  - OmniscientDB: a large language model-augmented DBMS that knows what other DBMSs do not know
T2  - Efl insights : an elf - the Data Science Institute publication
N2  - 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.
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/74426
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-744269
SN  - 1866-1238
SN  - 2700-2241
VL  - 2023
IS  - 2
SP  - 6
EP  - 8
PB  - E-Finance Lab e.V.
CY  - Frankfurt am Main
ER  -