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.

We introduce Explainable Scientific Research Assistant (ESRA), a literature discovery platform that augments search results with relevant details and explanations, aiding users in understanding more about their queries and the returned papers beyond existing literature search systems. Enabled by a knowledge graph we extracted from abstracts of 23k papers on the arXiv’s cs.CL category, ESRA provides three main features: explanation (for why a paper is returned to the user), list of facts (that are relevant to the query), and graph visualization (drawing connections between the query and each paper with surrounding related entities). The experimental results with humans involved show that ESRA can accelerate the users’ search process with paper explanations and helps them better explore the landscape of the topics of interest by exploiting the underlying knowledge graph. We provide the ESRA web application at http://esra.cp.eng.chula.ac.th/.