  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
