AI-PAVE-Br: Leveraging Large Language Models for Enhanced Product Attribute Value Extraction through a Golden Set Approach
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Computer Science > Computation and Language
arXiv:2606.24655 (cs)
[Submitted on 23 Jun 2026]
Title:AI-PAVE-Br: Leveraging Large Language Models for Enhanced Product Attribute Value Extraction through a Golden Set Approach
Authors:Murilo Gazzola, Hugo Gobato Souto, Samuel Silva, Júlia Schubert Peixoto, Felipe Siqueira, André Luis Pedroso de Morais, Caio Gomes
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Abstract:The explosive growth and complexity of product data within the dynamic Brazilian e-commerce landscape demand robust and specialized methods for structured information extraction. Traditional approaches to Product Attribute Value Extraction (PAVE) often struggle with the linguistic nuances and sheer diversity of product descriptions in Portuguese. To address this critical gap, this paper introduces two major contributions. First, we present AI-PAVEBr, a specialized system engineered with Large Language Models (LLMs) to perform high-accuracy PAVE specifically for Brazilian e-commerce catalogs. Second, to facilitate reproducible research and provide a definitive benchmark, we introduce and share the Golden Set, a new, meticulously curated, and manually annotated dataset for PAVE in Portuguese. We detail the creation process and structure (Entity, Category, Subcategories) of this high-quality reference set. Our experiments conclusively show that AI-PAVE-Br, leveraging targeted prompt engineering, dramatically outperforms conventional Named Entity Recognition (NER) baselines. This work not only delivers a superior, scalable solution for a major non-English market but also enriches the NLP community with a valuable, publicly available resource for future PAVE research.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF) |
| ACM classes: | I.2.7; I.2.6; I.2.1; H.3.1 |
| Cite as: | arXiv:2606.24655 [cs.CL] |
| (or arXiv:2606.24655v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24655
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the 15th Symposium in Information and Human Language Technology (STIL 2025), Brazilian Computer Society (SBC), 2025 |
| Related DOI: | https://doi.org/10.5753/stil.2025.37821
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View a PDF of the paper titled AI-PAVE-Br: Leveraging Large Language Models for Enhanced Product Attribute Value Extraction through a Golden Set Approach, by Murilo Gazzola and 6 other authors
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