@inproceedings{hongwimol-etal-2025-gavel,
    title = "{GAVEL}: Generative Attribute-Value Extraction Using {LLM}s on {LLM}-Augmented Datasets",
    author = "Hongwimol, Pollawat  and
      Sheng, Dong  and
      Zhang, Li  and
      Liu, Kai  and
      Wang, Xiufei",
    editor = "Shi, Weijia  and
      Yu, Wenhao  and
      Asai, Akari  and
      Jiang, Meng  and
      Durrett, Greg  and
      Hajishirzi, Hannaneh  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.knowledgenlp-1.6/",
    pages = "81--90",
    ISBN = "979-8-89176-229-9",
    abstract = "In the evolving e-commerce landscape, accurate product attribute-value extraction is crucial for enhancing user experience and increasing sales. This paper introduces GAVEL, a generative approach leveraging large language models (LLMs) to augment training data for attribute extraction from diverse textual sources. Our method extracts over 1,000 unique attributes across 2,000 product categories in multiple Southeast Asian languages, including Thai, Vietnamese, and Indonesian. Rigorous evaluations show significant improvements in accuracy and coverage compared to seller-provided attributes, with enhanced recall and F1 scores. Additionally, GAVEL reduces operational costs by minimizing instruction token usage and improves inference speed. The results of the A/B testing indicate that our model has a positive impact on Gross Merchandise Value (GMV) per page view (PV) across all three operating countries. This research highlights the potential of generative techniques for optimizing attribute extraction in multi-language e-commerce applications."
}