mlsanityData to 7 Oct 2026

ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction

arXiv 2404.15592 ↗First posted 24 Apr 2024cs.CV cs.AI cs.CLcited by 17

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

Existing datasets for attribute value extraction (AVE) predominantly focus on explicit attribute values while neglecting the implicit ones, lack product images, are often not publicly available, and lack an in-depth human inspection across diverse domains. To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction. ImplicitAVE, sourced from the MAVE dataset, is carefully curated and expanded to include implicit AVE and multimodality, resulting in a refined dataset of 68k training and 1.6k testing data across five domains. We also explore the application of multimodal large language models (MLLMs) to implicit AVE, establishing a comprehensive benchmark for MLLMs on the ImplicitAVE dataset. Six recent MLLMs with eleven variants are evaluated across diverse settings, revealing that implicit value extraction remains a challenging task for MLLMs. The contributions of this work include the development and release of ImplicitAVE, and the exploration and benchmarking of various MLLMs for implicit AVE, providing valuable insights and potential future research directions. Dataset and code are available at https://github.com/HenryPengZou/ImplicitAVE

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 13 of its 22 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

13 of 22 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Improved Baselines with Visual Instruction Tuning5 Oct
  2. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning11 May

Other references · 11

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  3. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,038
  4. 2018OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]cited by 62
  5. 2021MAVE: A Product Dataset for Multi-source Attribute Value Extractioncited by 53
  6. 2020Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Productcited by 39
  7. 2024LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extractioncited by 39
  8. 2021AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decodingcited by 32
  9. 2023MuseChat: A Conversational Music Recommendation System for Videoscited by 21
  10. 2024EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLMcited by 12
  11. 2023Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributescited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 7; 2026: 5.

17 papers.

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  1. 2024A Survey on Benchmarks of Multimodal Large Language Modelscited by 81
  2. 2024A Survey on Evaluation of Multimodal Large Language Modelscited by 46
  3. 2024A Survey on Multimodal Benchmarks: In the Era of Large AI Modelscited by 27
  4. 2024Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challengescited by 18
  5. 2024The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspectivecited by 14
  6. 2026Unveiling the Cognitive Compass: Theory-of-Mind-Guided Multimodal Emotion Reasoningcited by 8
  7. 2025LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendationcited by 7
  8. 2025GLEAN: Active Generalized Category Discovery with Diverse LLM Feedbackcited by 6
  9. 2025Visual Zero-Shot E-Commerce Product Attribute Value Extractioncited by 3
  10. 2025SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Servicescited by 2
  11. 2025MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extractioncited by 2
  12. 2026IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Productscited by 2
5 more
  1. 2025VideoAVE: A Multi-Attribute Video-to-Text Attribute Value Extraction Dataset and Benchmark Modelscited by 1
  2. 2026AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Constructioncited by 1
  3. 2026Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extractioncited by 1
  4. 2025SVG Decomposition for Enhancing Large Multimodal Models Visualization Comprehension: A Study with Floor Planscited by 0
  5. 2026CVE-SAI: Counterfactual Visual Evidence-Guided Selective Attribute Indexing for Risk-Controlled E-commerce Searchcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Correcting to Predict: Pseudo-Value Correction for Multimodal Attribute Value Extraction ↗
  2. 2024MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs
  3. 2024NEMO: Can Multimodal LLMs Identify Attribute-Modified Objects?
  4. 2025Vision Language Models as Values Detectors
  5. 2026Visual Search Augmented Chain-of-Thought Reasoning for Attribute Value Extraction from Product Videos
  6. 2024ARMADA: Attribute-Based Multimodal Data Augmentation
  7. 2025SO-Bench: A Structural Output Evaluation of Multimodal LLMs
  8. 2025For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs