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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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Document Type:
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Conference or workshop publication
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Year of publication:
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2025
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Book title:
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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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The title of a journal, publication series:
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Lecture Notes in Computer Science
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Volume:
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15342
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Page range:
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38-52
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Conference title:
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iiWAS, International Conference on Information Integration and Web Intelligence
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Location of the conference venue:
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Bratislava, Slovakia
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Date of the conference:
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02.-04.12.2024
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Publisher:
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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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Place of publication:
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Berlin [u.a.]
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Publishing house:
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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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Related URLs:
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Information Systems V: Web-based Systems (Bizer 2012-)
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Subject:
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004 Computer science, internet
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Keywords (English):
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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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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
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