AI-PAVE-Br applies LLMs with prompt engineering to outperform NER baselines on Portuguese product attribute extraction and releases the Golden Set as a new benchmark dataset.
Using LLMs for the Extraction and Normalization of Product Attribute Values
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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Hybrid scraping plus LLM extraction with embedding-based verification for robust unstructured web data aggregation into JSON.
citing papers explorer
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AI-PAVE-Br: Leveraging Large Language Models for Enhanced Product Attribute Value Extraction through a Golden Set Approach
AI-PAVE-Br applies LLMs with prompt engineering to outperform NER baselines on Portuguese product attribute extraction and releases the Golden Set as a new benchmark dataset.
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Method for Aggregating Unstructured Data Using Large Language Models
Hybrid scraping plus LLM extraction with embedding-based verification for robust unstructured web data aggregation into JSON.