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

arXiv: https://arxiv.org/abs/2404.15592 · HTML: https://mlsanity.com/paper/2404.15592/

## Key facts

- Cited by 17 papers in the map (data through 14 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.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (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)
- [Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product](https://mlsanity.com/paper/2009.07162/) (2020)
- [LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction](https://mlsanity.com/paper/2403.00863/) (2024)
- [AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding](https://mlsanity.com/paper/2106.02318/) (2021)
- [MuseChat: A Conversational Music Recommendation System for Videos](https://mlsanity.com/paper/2310.06282/) (2023)
- [EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM](https://mlsanity.com/paper/2404.08886/) (2024)
- [Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributes](https://mlsanity.com/paper/2306.00379/) (2023)

## Built on by (most cited first, 17 of 17)

- [A Survey on Benchmarks of Multimodal Large Language Models](https://mlsanity.com/paper/2408.08632/) (2024)
- [A Survey on Evaluation of Multimodal Large Language Models](https://mlsanity.com/paper/2408.15769/) (2024)
- [A Survey on Multimodal Benchmarks: In the Era of Large AI Models](https://mlsanity.com/paper/2409.18142/) (2024)
- [Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges](https://mlsanity.com/paper/2409.09927/) (2024)
- [The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective](https://mlsanity.com/paper/2407.08583/) (2024)
- [Unveiling the Cognitive Compass: Theory-of-Mind-Guided Multimodal Emotion Reasoning](https://mlsanity.com/paper/2602.00971/) (2026)
- [LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation](https://mlsanity.com/paper/2503.01814/) (2025)
- [GLEAN: Active Generalized Category Discovery with Diverse LLM Feedback](https://mlsanity.com/paper/2502.18414/) (2025)
- [Visual Zero-Shot E-Commerce Product Attribute Value Extraction](https://mlsanity.com/paper/2502.15979/) (2025)
- [IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products](https://mlsanity.com/paper/2606.14383/) (2026)
- [SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services](https://mlsanity.com/paper/2505.23065/) (2025)
- [MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction](https://mlsanity.com/paper/2510.05611/) (2025)
- [VideoAVE: A Multi-Attribute Video-to-Text Attribute Value Extraction Dataset and Benchmark Models](https://mlsanity.com/paper/2508.11801/) (2025)
- [AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction](https://mlsanity.com/paper/2604.16950/) (2026)
- [Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction](https://mlsanity.com/paper/2604.26209/) (2026)
- [SVG Decomposition for Enhancing Large Multimodal Models Visualization Comprehension: A Study with Floor Plans](https://mlsanity.com/paper/2511.03478/) (2025)
- [CVE-SAI: Counterfactual Visual Evidence-Guided Selective Attribute Indexing for Risk-Controlled E-commerce Search](https://mlsanity.com/paper/2608.25023/) (2026)

## 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