mlsanityData to 13 Oct 2026

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

arXiv 2112.08663 ↗First posted 16 Dec 2021cs.CL cs.IRcited by 53

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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.

Key facts. Cited by 53 papers in the map (data through 13 Oct 2026). Builds on 24 of its 63 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

24 of 63 references.

References · 24

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12 more
  1. 2020Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Productcited by 39
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Citing papers per year: 2022: 5; 2023: 11; 2024: 15; 2025: 12; 2026: 10.

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  1. 2024eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Datacited by 44
  2. 2024LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extractioncited by 39
  3. 2022WebFormer: The Web-page Transformer for Structure Information Extractioncited by 34
  4. 2022FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discoverycited by 23
  5. 2023ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extractioncited by 20
  6. 2024Using LLMs for the Extraction and Normalization of Product Attribute Valuescited by 20
  7. 2024Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Modelscited by 20
  8. 2024ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extractioncited by 17
  9. 2022Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasetscited by 12
  10. 2024EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLMcited by 12
  11. 2023A Unified Generative Approach to Product Attribute-Value Identificationcited by 10
  12. 2024A survey on fairness of large language models in e-commerce: progress, application, and challengecited by 10
38 more
  1. 2025Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branchingcited by 10
  2. 2024Natural Language Processing for Human Resources: A Surveycited by 9
  3. 2023AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributescited by 8
  4. 2024An Empirical Comparison of Generative Approaches for Product Attribute-Value Identificationcited by 7
  5. 2025MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understandingcited by 7
  6. 2023Towards Open-World Product Attribute Mining: A Lightly-Supervised Approachcited by 6
  7. 2024Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Modelscited by 6
  8. 2024Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Datacited by 6
  9. 2025TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identificationcited by 6
  10. 2022Deep Partial Multiplex Network Embeddingcited by 5
  11. 2023PUMGPT: A Large Vision-Language Model for Product Understandingcited by 5
  12. 2026Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agentscited by 5
  13. 2024EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interactioncited by 4
  14. 2023Product Information Extraction using ChatGPTcited by 3
  15. 2023Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extractioncited by 3
  16. 2023SAGE: Structured Attribute Value Generation for Billion-Scale Product Catalogscited by 3
  17. 2024Exploring Large Language Models for Product Attribute Value Identificationcited by 3
  18. 2025Visual Zero-Shot E-Commerce Product Attribute Value Extractioncited by 3
  19. 2025A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplacescited by 3
  20. 2024Multi-Conditional Ranking with Large Language Modelscited by 2
  21. 2025Self-Refinement Strategies for LLM-based Product Attribute Value Extractioncited by 2
  22. 2025MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extractioncited by 2
  23. 2026IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Productscited by 2
  24. 2022ImPaKT: A Dataset for Open-Schema Knowledge Base Constructioncited by 1
  25. 2023ForeSeer: Product Aspect Forecasting Using Temporal Graph Embeddingcited by 1
  26. 2023JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extractioncited by 1
  27. 2025Zero-Shot Open-Schema Entity Structure Discoverycited by 1
  28. 2025VideoAVE: A Multi-Attribute Video-to-Text Attribute Value Extraction Dataset and Benchmark Modelscited by 1
  29. 2025Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identificationcited by 1
  30. 2025Towards Cross-Modal Error Detection with Tables and Imagescited by 1
  31. 2025Attribute-Aware Controlled Product Generation with LLMs for E-commercecited by 1
  32. 2026Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extractioncited by 1
  33. 2023Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Datacited by 0
  34. 2024A Framework for Leveraging Partially-Labeled Data for Product Attribute-Value Identificationcited by 0
  35. 2026JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applicationscited by 0
  36. 2026CatalogAgent: A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Modelscited by 0
  37. 2026TRACE: Agentic Catalog Enrichment with Multi-source Evidence Groundingcited by 0
  38. 2026DynaContext: Self-Improving Dynamic Contextualization of Optimized Prompts for Heterogeneous Parameter Extractioncited by 0

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