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ExtractGPT: Exploring the potential of Large Language Models for product attribute value extraction
Brinkmann, Alexander
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Shraga, Roee
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Bizer, Christian
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2025
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Buchtitel:
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Information integration and web intelligence : 26th International Conference, iiWAS 2024, Bratislava, Slovak Republic, December 2–4, 2024, Proceedings. Part I
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Titel einer Zeitschrift oder einer Reihe:
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Lecture Notes in Computer Science
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Band/Volume:
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15342
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Seitenbereich:
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38-52
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Veranstaltungstitel:
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iiWAS, International Conference on Information Integration and Web Intelligence
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Veranstaltungsort:
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Bratislava, Slovakia
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Veranstaltungsdatum:
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02.-04.12.2024
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Herausgeber:
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Delir Haghighi, Pari
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Greguš, Michal
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Kotsis, Gabriele
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Khalil, Ismail
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Ort der Veröffentlichung:
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Berlin [u.a.]
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Verlag:
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Springer
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ISBN:
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978-3-031-78090-5
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ISSN:
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0302-9743 , 1611-3349
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Verwandte URLs:
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Information Systems V: Web-based Systems (Bizer 2012-)
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Fachgebiet:
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004 Informatik
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Freie Schlagwörter (Englisch):
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information extraction , product attribute value extraction , Large Language Models
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Abstract:
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E-commerce platforms require structured product data in the form of attribute-value pairs to offer features such as faceted product search or attribute-based product comparison. However, vendors often provide unstructured product descriptions, necessitating the extraction of attribute-value pairs from these texts. BERT-based extraction methods require large amounts of task-specific training data and struggle with unseen attribute values. This paper explores using large language models (LLMs) as a more training-data efficient and robust alternative. We propose prompt templates for zero-shot and few-shot scenarios, comparing textual and JSON-based target schema representations. Our experiments show that GPT-4 achieves the highest average F1-score of 85% using detailed attribute descriptions and demonstrations. Llama-3-70B performs nearly as well, offering a competitive open-source alternative. GPT-4 surpasses the best PLM baseline by 5% in F1-score. Fine-tuning GPT-3.5 increases the performance to the level of GPT-4 but reduces the model's ability to generalize to unseen attribute values.
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