Column type annotation using ChatGPT

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URL:	https://ceur-ws.org/Vol-3462/TADA1.pdf
URN:	urn:nbn:de:bsz:180-madoc-651328
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2023
Buchtitel:	Joint proceedings of workshops at the 49th International Conference on Very Large Data Bases (VLDB 2023), Vancouver, Canada, August 28 - September 1, 2023, VLDBW 2023
Titel einer Zeitschrift oder einer Reihe:	CEUR Workshop Proceedings
Band/Volume:	3462
Seitenbereich:	1-12
Veranstaltungstitel:	Workshop on Tabular Data Analysis (TaDA) @ VLDB 2023
Veranstaltungsort:	Vancouver, Canada
Veranstaltungsdatum:	01.09.2023
Herausgeber:	Bordawekar, Rajesh ; Cappiello, Cinzia ; Efthymiou, Vasilis
Ort der Veröffentlichung:	Aachen, Germany
Verlag:	RWTH Aachen
ISSN:	1613-0073
Verwandte URLs:	https://ceur-ws.org/Vol-3462/ https://vldb.org/2023/ https://vldb.org/2023/?program-structure
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Information Systems V: Web-based Systems (Bizer 2012-)
Bereits vorhandene Lizenz:	Creative Commons Namensnennung 4.0 International (CC BY 4.0)
Fachgebiet:	004 Informatik
Freie Schlagwörter (Englisch):	table annotation , column type annotation , LLMs , ChatGPT
Abstract:	Column type annotation is the task of annotating the columns of a relational table with the semantic type of the values contained in each column. Column type annotation is an important pre-processing step for data search and data integration in the context of data lakes. State-of-the-art column type annotation methods either rely on matching table columns to properties of a knowledge graph or fine-tune pre-trained language models such as BERT for column type annotation. In this work, we take a different approach and explore using ChatGPT for column type annotation. We evaluate different prompt designs in zero- and few-shot settings and experiment with providing task definitions and detailed instructions to the model. We further implement a two-step table annotation pipeline which first determines the class of the entities described in the table and depending on this class asks ChatGPT to annotate columns using only the relevant subset of the overall vocabulary. Using instructions as well as the two-step pipeline, ChatGPT reaches F1 scores of over 85% in zero- and one-shot setups. To reach a similar F1 score a RoBERTa model needs to be fine-tuned with 356 examples. This comparison shows that ChatGPT is able deliver competitive results for the column type annotation task given no or only a minimal amount of task-specific demonstrations.