MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models
Journal:
arXiv
Published Date:
May 23, 2025
Abstract
Memes have emerged as a popular form of multimodal online communication,
where their interpretation heavily depends on the specific context in which
they appear. Current approaches predominantly focus on isolated meme analysis,
either for harmful content detection or standalone interpretation, overlooking
a fundamental challenge: the same meme can express different intents depending
on its conversational context. This oversight creates an evaluation gap:
although humans intuitively recognize how context shapes meme interpretation,
Large Vision Language Models (LVLMs) can hardly understand context-dependent
meme intent. To address this critical limitation, we introduce MemeReaCon, a
novel benchmark specifically designed to evaluate how LVLMs understand memes in
their original context. We collected memes from five different Reddit
communities, keeping each meme's image, the post text, and user comments
together. We carefully labeled how the text and meme work together, what the
poster intended, how the meme is structured, and how the community responded.
Our tests with leading LVLMs show a clear weakness: models either fail to
interpret critical information in the contexts, or overly focus on visual
details while overlooking communicative purpose. MemeReaCon thus serves both as
a diagnostic tool exposing current limitations and as a challenging benchmark
to drive development toward more sophisticated LVLMs of the context-aware
understanding.
