MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models

TL;DR

MemeReaCon introduces a benchmark to evaluate large vision-language models' ability to understand memes within their original conversational context, highlighting current models' limitations in interpreting context-dependent meme intent.

Contribution

We created MemeReaCon, a new benchmark dataset that assesses how well LVLMs understand memes in context, addressing a key gap in current multimodal understanding research.

Findings

LVLMs struggle with context-dependent meme interpretation

Models tend to focus on visual details over communicative intent

MemeReaCon exposes significant limitations in current LVLMs' contextual understanding

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.