GAVEL : Generative Attribute-Value Extraction Using LLM s on LLM -Augmented Datasets (2025)

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.

## introduction product attributes significantly influence product search @xcite , recommendation systems @xcite , and buyers' decision-making processes @xcite @xcite . inadequate or erroneous information on product pages can lead to adverse outcomes, such as a poor shopping experience, decreased visibility, and lower sales. for instance, figure 1 illustrates a case where the color 'sky blue' is mentioned in the title, highlights, and description; however, it contradicts the stock keeping unit (sku) variant, which is listed as red. such discrepancies can confuse potential buyers and negatively impact their purchasing decisions. figure 1 : an example of seller-provided information, including extracted attributes. the information consists of (1) category, (2) images, (3) title, (4) sku variants, (5) highlights, (6) description, and (7) specifications. in this case, the color 'sky blue' appears in the title, highlights, and description; however, it contradicts the sku variant's color, which is red. in recent years, there has been significant research on product attribute-value extraction (pave) @xcite @xcite . initial studies primarily employed sequence tagging methods using encoderonly models like bert @xcite @xcite @xcite . however, this approach has limitations in handling unseen and canonicalized values @xcite . to overcome these challenges, subsequent research has shifted from sequence tagging to sequence-tosequence generation models, such as t5 and bart, which support more flexible output formats @xcite @xcite @xcite @xcite . additionally, current research leverages the zero-shot and few-shot capabilities of large-scale autoregressive models, such as gpt-3.5 and gpt-4, to enhance attribute extraction processes @xcite @xcite . this evolution has demonstrated that effective attribute value extraction significantly improves key e-commerce metrics, including click-through rate (ctr) and add-to-cart rate (atc) @xcite . despite these advancements, existing publicly available datasets for pave face several limitations. for instance, the ae-110k dataset @xcite is confined to the sports & entertainment category, offering data in the form of title-attribute-value triplets. while mepave @xcite broadens its data sources to include images and descriptions, it still possesses a limited number of unique attributes. oa-mine @xcite encompasses titles for 100 product types but lacks comprehensive information beyond the title itself. mave @xcite includes 1,257 categories and various input types; however, it requires that explicit attribute values be present in the text, which complicates the extraction of unseen and canonicalized values. although implicitave @xcite aims to address this shortcoming through a two-round human inspection process that annotates 25 attributes across five domains, it remains limited to titles and images. furthermore, existing datasets predominantly rely on item-level information, which may lead to inaccuracies in attribute value extraction for a specific sku variant, as demonstrated in figure 1 . expanding beyond these challenges, it is crucial to recognize the growing e-commerce landscape in southeast asia, where the need for pave solutions is becoming increasingly pressing. this region is home to a rich diversity of languages, including thai, vietnamese, indonesian, and english. however, most pave research to date has primarily focused on english @xcite @xcite , chinese @xcite , and japanese @xcite . to the best of our knowledge, there has been no prior study exploring the potential for enhancing pave in southeast asian languages. to tackle these challenges, we propose an efficient pipeline for augmenting training data for generative attribute-value extraction, as depicted in figure 2 . our key contributions are summarized as follows: ## experimental setup in this section, we outline the experimental framework employed in this study, utilizing data sourced from lazada, a prominent e-commerce platform in southeast asia. the information collected encompasses product profiles and various attributes relevant to our analysis, establishing a comprehensive basis for the subsequent investigations. ## results this section presents performance metrics and evaluations of four selected llms across three datasets. we benchmark the models against the seller attribute values and assess their effectiveness in predicting multiple attributes, the quality of the first predicted value, and inference speed. ## conclusion in this study, we introduced an innovative approach for attribute-value extraction by leveraging generative llms on augmented datasets. our method capitalizes on the zero-shot capabilities of advanced llms, facilitating the extraction of over 1,000 unique attributes across diverse categories with enhanced accuracy and speed. the empirical results demonstrate significant improvements in the quality of attributes provided by sellers, with notable increases in accuracy, coverage, and overall market performance metrics. by fine-tuning smaller models, we not only reduced operational costs but also enhanced efficiency, allowing for rapid inference while maintaining high prediction quality. the successful outcomes from our experiments underscore the viability of our gavel pipeline for wide-scale implementation in multilingual e-commerce platforms. this research paves the way for further exploration of generative approaches to attribute extraction, offering organizations valuable insights into optimizing their inventory and enhancing customer experiences.