The paper presents a hybrid generative-tagging decoding approach for extracting product attribute-value pairs in e-commerce. It combines a tagging head that directly copies frequent attribute values from the source text with a generative head that handles rare attributes and non-copyable values. Experiments integrated with vLLM inference show a 10% increase in attribute coverage, a 63% rise in correct extractions, and a 44% reduction in latency without sacrificing precision.
By Keshav Kolluru, Manas Gupta, Maroof Ahmad, Suchitra Sathyanarayana
arXiv:2606. 24655v1 Announce Type: cross 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.
By Murilo Gazzola, Hugo Gobato Souto, Samuel Silva, J\'ulia Schubert Peixoto, Felipe Siqueira, Andr\'e Luis Pedroso de Morais, Caio Gomes
The paper presents a two‑stage large language model pipeline for e‑commerce attribute extraction. First, it discovers a compact, ranked set of purchase‑discriminative attributes for each product category; second, it extracts those attribute values from catalog text using a fine‑tuned Qwen3‑4B model with Hyper‑Parallel Decoding. The approach attains 85% extraction accuracy while cutting inference costs by 92%, enabling scalable production use and producing consistent, comparable product knowledge bases.
By Nikhita Vedula, Dushyanta Dhyani, Bryan Wang, Shervin Malmasi
arXiv:2607. 07469v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) for e-commerce attribute extraction requires labeled data representative across thousands of product types, attributes, and multiple languages.
By Andrea Scarinci, Virginia Negri, Brayan Impata, Suleiman Khan, Victor Martinez, Marcello Federico
arXiv:2610.09063v1 Announce Type: new
Abstract: Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability c...
By Sourabh Kasliwal, Shubhranshu Singh
The paper introduces NORA, a noise‑robust tagging system for Financial Numerical Entity (FNE) attributes, addressing errors in XBRL‑derived labels and under‑emphasized attributes such as reporting‑time relation, measurement scale, and accounting sign. NORA employs task‑aware instance‑specific weighting to reduce the impact of noisy labels during training and proposes an evaluation protocol that filters noisy labels in real‑world test sets. A large benchmark of 11.4 million FNEs across 6.3 million sentences is constructed, and experiments show NORA outperforms prior models in mean Accuracy and Macro F1, especially for time‑relation prediction.
By Sheng-Hua Tsai, Chen-Yang Lai, Yi-Jhen Li, Ju-Chun Yen, Hsin-Min Lu
arXiv:2607. 14396v1 Announce Type: new Abstract: Product catalogs are the backbone of e-commerce sites, yet a large number of structured attributes (SAs) -- such as material, color, and shape -- often have missing values.
By Zhu Cheng (Xuan), Zhenming Wang (Xuan), Yu (Xuan), Tang, Dan Liu, Bryan Zhang, Athanasios N. Nikolakopoulos, Pranav Souri Itabada, Jing Zhang, Chih-Chi Chou, Peng Gao, Fatemeh Mansoori, Bharat Bojja, Sarath Chander, Sameer Thombare, Umit Batur, Tarik Arici
arXiv:2412.05655v2 Announce Type: replace
Abstract: This paper presents a case study on the development of Auto-AdvER, a specialised span-labelling schema, along with its corresponding dataset for th...
By Filippos Ventirozos, Ioanna Nteka, Tania Nandy, Jozef Baca, Charlie Roadhouse, Peter A. Appleby, Matthew Shardlow
arXiv:2609.37127v1 Announce Type: new
Abstract: Establishing unbranding as a critical practice to prevent visual logos from acquiring negative connotations is standard in image generation. Large Lang...
By Kajetan O\.z\'og, Alicja Wojciechowska, Dawid Malarz, Pawe{\l} Batorski, Artur Kasymov, Przemys{\l}aw Spurek
arXiv:2610.17368v1 Announce Type: cross
Abstract: Generative recommendation models have shown promising results by representing items as semantic IDs and generating these IDs autoregressively. We int...
By Tripti Shukla, Shwetha Somasundaram, Akshat Gupta, Rohit Garg, Paul Duff, Ian Anderson
arXiv:2610.20603v1 Announce Type: new
Abstract: We present CommerceGen-Studio, an end-to-end framework for generating commercial-grade main images for e-commerce Stock Keeping Units (SKUs). Our frame...
By Xiaoli Xu, Zhengtao Wu, Zhengze Xu, Tao Liu, Yongchao Du, Zuan Gao, Xingjian Wang, Hao Yan, Mengting Chen, Wei Li, Bo Zheng
arXiv:2609.07334v1 Announce Type: new
Abstract: The Asset Administration Shell (AAS) is a cornerstone of Industry 4.0 and the Digital Product Passport, providing standardized digital representations...
By Janek Gro{\ss}, Jens Heidrich