Leveraging Product Catalog Patterns for Multilingual E-commerce Product Attribute Prediction (2025.emnlp-industry)
Copied to clipboard
| Challenge: | E-commerce stores increasingly use Large Language Models to improve catalog data quality . a critical challenge is accurately predicting missing structured attribute values . |
| Approach: | They propose a retrieval-augmented system that leverages existing product catalog entries to guide LLM predictions for missing attributes. |
| Outcome: | The proposed system improves catalog data quality by 34% and accuracy by 0.8% . the proposed model can predict missing attributes in multilingual product catalogs . |
Similar Papers
XRAG: Cross-lingual Retrieval-Augmented Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | XRAG evaluates the generation abilities of LLMs in cross-lingual RAG settings where the user language does not match retrieval results. |
| Approach: | They propose a benchmark to evaluate the generation abilities of LLMs in cross-lingual RAG settings where the user language does not match retrieval results. |
| Outcome: | XRAG is a benchmark designed to evaluate the generation abilities of LLMs in cross-lingual RAG settings where the user language does not match retrieval results. |
AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction (2026.findings-acl)
Copied to clipboard
Pollawat Hongwimol, Haoning Shang, Chutong Wang, Zhichao Wan, Yi Gao, Yuanming Li, Lin Gui, Wenhao Sun, Cheng Yu
| Challenge: | Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain. |
| Approach: | They propose a multi-agent Large Language Model framework that constructs a Product-attribute Knowledge Graph from multimodal product content. |
| Outcome: | The proposed framework achieves 0.953 WKE for product types, 0.724 WKEs for attribute keys, and 0.531 edge-level accuracy for value assertions after canonicalization on a large real-world marketplace catalog dataset from Lazada (Alibaba). |
Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)
Copied to clipboard
Huike Zou, Haiyang Yang, Yindu Su, Chen Li Yu, Qinye Xie, Chengbao Lian, Qingheng Zhang, Shuguang Han, Fei Huang, Jufeng Chen
| Challenge: | Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability. |
| Approach: | They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain. |
| Outcome: | The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset. |
eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables (2025.naacl-industry)
Copied to clipboard
| Challenge: | eC-Tab2Text dataset is designed to capture product attributes and user-specific queries. |
| Approach: | They propose a novel dataset to capture the intricacies of e-commerce including detailed product attributes and user-specific queries. |
| Outcome: | The proposed dataset outperforms existing generalpurpose LLMs in generating accurate product reviews. |
PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights (2025.acl-demo)
Copied to clipboard
| Challenge: | Accurate and complete product descriptions are laborious to sift through manually. |
| Approach: | They propose a system that uses Large Language Models to extract, compare, and structure insights from customer reviews and seller descriptions. |
| Outcome: | The proposed system can extract, compare, and structure insights from customer reviews and seller descriptions. |
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product (2020.emnlp-main)
Copied to clipboard
| Challenge: | In the real world, product attribute values are incomplete and vary over time, which hinders practical applications. |
| Approach: | They propose a multimodal method to jointly predict product attributes and extract values from product images using multimodal product information. |
| Outcome: | The proposed method can predict product attributes and extract values from product images with the help of product images. |
Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributes (2023.acl-industry)
Copied to clipboard
| Challenge: | E-commerce websites often don’t label or mislabel attributes of products . |
| Approach: | They propose a multi-modal product attribute generation system that extracts product attributes from the product pages of eCommerce stores by using both text and images. |
| Outcome: | The proposed model improves the recall@90P accuracy by 10.16% and 6.9 from the state-of-the-art models. |
Open-World Attribute Mining for E-Commerce Products with Multimodal Self-Correction Instruction Tuning (2025.acl-long)
Copied to clipboard
| Challenge: | Current AM methods focus on extracting attributes from unimodal text, underutilizing multimodal data. |
| Approach: | They propose a framework for multimodal self-correction instruction tuning to extract new attributes from images and text with Multimodal Large Language Models. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two datasets. |
Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Our system generates and refines prompts for evaluating attribute quality across tens of thousands of product category–attribute pairs. |
| Approach: | They propose a free cascade for auto-prompting Large Language Models (LLMs) that generates and refines prompts for evaluating attribute quality across tens of thousands of product category–attribute pairs. |
| Outcome: | The proposed system improves precision and recall by 8–10% over chain-of-thought prompting while reducing domain expert effort from 5.1 hours to 3 minutes per attribute. |
Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)
Copied to clipboard
Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Zou, Peng Dai, Roberto Galan, Michael Porter, Dongmei Jia, Ning Zhang, Lian Xiong
| Challenge: | Existing fashion recommendation systems struggle with the unique challenges of the fashion domain. |
| Approach: | They propose a sequential fashion recommendation framework that leverages a pre-trained large language model enhanced with recommendation-specific prompts. |
| Outcome: | The proposed framework significantly improves fashion recommendation performance on Amazon fashion. |