# MAVE: A Product Dataset for Multi-source Attribute Value Extraction

arXiv: https://arxiv.org/abs/2112.08663 · HTML: https://mlsanity.com/paper/2112.08663/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017)
- [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018)
- [Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product](https://mlsanity.com/paper/2009.07162/) (2020)
- [DenserNet: Weakly Supervised Visual Localization Using Multi-scale Feature Aggregation](https://mlsanity.com/paper/2012.02366/) (2020)
- [AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding](https://mlsanity.com/paper/2106.02318/) (2021)
- [TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories](https://mlsanity.com/paper/2004.13852/) (2020)
- [Product-Aware Answer Generation in E-Commerce Question-Answering](https://mlsanity.com/paper/1901.07696/) (2019)
- [PAM: Understanding Product Images in Cross Product Category Attribute Extraction](https://mlsanity.com/paper/2106.04630/) (2021)
- [K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce](https://mlsanity.com/paper/2104.06960/) (2021)
- [Attribute Extraction from Product Titles in eCommerce](https://mlsanity.com/paper/1608.04670/) (2016)
- [Multimodal Attribute Extraction](https://mlsanity.com/paper/1711.11118/) (2017)
- [Advances in Natural Language Question Answering: A Review](https://mlsanity.com/paper/1904.05276/) (2019)
- [Semi-supervisedly Co-embedding Attributed Networks](https://mlsanity.com/paper/1910.14491/) (2019)
- [Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning](https://mlsanity.com/paper/2010.09189/) (2020)

## Built on by (most cited first, 30 of 53)

- [eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data](https://mlsanity.com/paper/2402.08831/) (2024)
- [LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction](https://mlsanity.com/paper/2403.00863/) (2024)
- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery](https://mlsanity.com/paper/2211.08316/) (2022)
- [ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction](https://mlsanity.com/paper/2310.12537/) (2023)
- [Using LLMs for the Extraction and Normalization of Product Attribute Values](https://mlsanity.com/paper/2403.02130/) (2024)
- [Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models](https://mlsanity.com/paper/2410.20745/) (2024)
- [ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction](https://mlsanity.com/paper/2404.15592/) (2024)
- [Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets](https://mlsanity.com/paper/2211.16878/) (2022)
- [EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM](https://mlsanity.com/paper/2404.08886/) (2024)
- [A Unified Generative Approach to Product Attribute-Value Identification](https://mlsanity.com/paper/2306.05605/) (2023)
- [A survey on fairness of large language models in e-commerce: progress, application, and challenge](https://mlsanity.com/paper/2405.13025/) (2024)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching](https://mlsanity.com/paper/2502.00969/) (2025)
- [AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes](https://mlsanity.com/paper/2305.14725/) (2023)
- [An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification](https://mlsanity.com/paper/2407.01137/) (2024)
- [MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding](https://mlsanity.com/paper/2508.11999/) (2025)
- [Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach](https://mlsanity.com/paper/2305.18350/) (2023)
- [PUMGPT: A Large Vision-Language Model for Product Understanding](https://mlsanity.com/paper/2308.09568/) (2023)
- [Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models](https://mlsanity.com/paper/2404.14772/) (2024)
- [Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data](https://mlsanity.com/paper/2410.17337/) (2024)
- [TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2501.03835/) (2025)
- [Deep Partial Multiplex Network Embedding](https://mlsanity.com/paper/2203.02656/) (2022)
- [Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agents](https://mlsanity.com/paper/2603.18516/) (2026)
- [EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction](https://mlsanity.com/paper/2406.06839/) (2024)
- [Product Information Extraction using ChatGPT](https://mlsanity.com/paper/2306.14921/) (2023)
- [Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction](https://mlsanity.com/paper/2308.08413/) (2023)
- [SAGE: Structured Attribute Value Generation for Billion-Scale Product Catalogs](https://mlsanity.com/paper/2309.05920/) (2023)
- [Exploring Large Language Models for Product Attribute Value Identification](https://mlsanity.com/paper/2409.12695/) (2024)
- [Visual Zero-Shot E-Commerce Product Attribute Value Extraction](https://mlsanity.com/paper/2502.15979/) (2025)

## Abstract (arXiv)

Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product ranking, retrieval and recommendations. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, we introduce MAVE, a new dataset to better facilitate research on product attribute value extraction. MAVE is composed of a curated set of 2.2 million products from Amazon pages, with 3 million attribute-value annotations across 1257 unique categories. MAVE has four main and unique advantages: First, MAVE is the largest product attribute value extraction dataset by the number of attribute-value examples. Second, MAVE includes multi-source representations from the product, which captures the full product information with high attribute coverage. Third, MAVE represents a more diverse set of attributes and values relative to what previous datasets cover. Lastly, MAVE provides a very challenging zero-shot test set, as we empirically illustrate in the experiments. We further propose a novel approach that effectively extracts the attribute value from the multi-source product information. We conduct extensive experiments with several baselines and show that MAVE is an effective dataset for attribute value extraction task. It is also a very challenging task on zero-shot attribute extraction. Data is available at it urlhttps://github.com/google-research-datasets/MAVE.