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.
| 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: | Universitätsbibliothek 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: | Universitätspublikationen |
| Licence (German): |  Deutsches Urheberrecht |
