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

翻译：现有面向属性值提取（AVE）的数据集主要关注显式属性值，而忽略了隐式属性值，缺乏产品图像，通常不公开可用，且缺乏跨不同领域的深度人工审查。为解决这些局限性，我们提出了ImplicitAVE——首个公开可用的隐式属性值提取多模态数据集。ImplicitAVE源自MAVE数据集，经过精心筛选和扩展，融合了隐式AVE与多模态特性，最终形成包含五个领域68k训练数据与1.6k测试数据的精炼数据集。我们同时探索了多模态大语言模型（MLLMs）在隐式AVE中的应用，在ImplicitAVE数据集上建立了针对MLLMs的全面基准。我们评估了六种近期MLLMs的十一个变体在不同设置下的表现，结果表明隐式值提取对MLLMs而言仍是一项具有挑战性的任务。本工作的贡献包括：开发并发布ImplicitAVE，探索并基准测试多种MLLMs在隐式AVE中的性能，为未来研究提供宝贵见解与潜在方向。数据集与代码已开源：https://github.com/HenryPengZou/ImplicitAVE