# Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification

arXiv: https://arxiv.org/abs/2509.23874 · HTML: https://mlsanity.com/paper/2509.23874/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 11 of 11)

- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018)
- [MAVE: A Product Dataset for Multi-source Attribute Value Extraction](https://mlsanity.com/paper/2112.08663/) (2021)
- [AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding](https://mlsanity.com/paper/2106.02318/) (2021)
- [Product-Aware Answer Generation in E-Commerce Question-Answering](https://mlsanity.com/paper/1901.07696/) (2019)
- [QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction](https://mlsanity.com/paper/2108.08468/) (2021)
- [A Unified Generative Approach to Product Attribute-Value Identification](https://mlsanity.com/paper/2306.05605/) (2023)
- [An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification](https://mlsanity.com/paper/2407.01137/) (2024)
- [TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2501.03835/) (2025)
- [IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing](https://mlsanity.com/paper/2410.16977/) (2024)
- [Toward Optimal Search and Retrieval for RAG](https://mlsanity.com/paper/2411.07396/) (2024)

## Built on by (most cited first, 1 of 1)

- [JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications](https://mlsanity.com/paper/2606.28070/) (2026)

## Abstract (arXiv)

Identifying attribute values from product profiles is a key task for improving product search, recommendation, and business analytics on e-commerce platforms, which we called Product Attribute Value Identification (PAVI) . However, existing PAVI methods face critical challenges, such as cascading errors, inability to handle out-of-distribution (OOD) attribute values, and lack of generalization capability. To address these limitations, we introduce Multi-Value-Product Retrieval-Augmented Generation (MVP-RAG), combining the strengths of retrieval, generation, and classification paradigms. MVP-RAG defines PAVI as a retrieval-generation task, where the product title description serves as the query, and products and attribute values act as the corpus. It first retrieves similar products of the same category and candidate attribute values, and then generates the standardized attribute values. The key advantages of this work are: (1) the proposal of a multi-level retrieval scheme, with products and attribute values as distinct hierarchical levels in PAVI domain (2) attribute value generation of large language model to significantly alleviate the OOD problem and (3) its successful deployment in a real-world industrial environment. Extensive experimental results demonstrate that MVP-RAG performs better than the state-of-the-art baselines.