# I Came, I Saw, I Explained: Benchmarking Multimodal LLMs on Figurative Meaning in Memes

arXiv: https://arxiv.org/abs/2603.23229 · HTML: https://mlsanity.com/paper/2603.23229/

## Key facts

- Cited by 2 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 15 of its 47 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## 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, 15 of 15)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://mlsanity.com/paper/2209.06293/) (2022)
- [FLUTE: Figurative Language Understanding through Textual Explanations](https://mlsanity.com/paper/2205.12404/) (2022)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)
- [Testing the Ability of Language Models to Interpret Figurative Language](https://mlsanity.com/paper/2204.12632/) (2022)
- [Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection](https://mlsanity.com/paper/2110.10949/) (2021)
- [Investigating Robustness of Dialog Models to Popular Figurative Language Constructs](https://mlsanity.com/paper/2110.00687/) (2021)
- [Understanding Figurative Meaning through Explainable Visual Entailment](https://mlsanity.com/paper/2405.01474/) (2024)
- [MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection](https://mlsanity.com/paper/2507.06908/) (2025)
- [A Template Is All You Meme](https://mlsanity.com/paper/2311.06649/) (2023)
- [Social Meme-ing: Measuring Linguistic Variation in Memes](https://mlsanity.com/paper/2311.09130/) (2023)
- [Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!](https://mlsanity.com/paper/2404.06357/) (2024)
- [What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse](https://mlsanity.com/paper/2505.16592/) (2025)

## Built on by (most cited first, 2 of 2)

- [MemeBench: What LVLMs Miss When Interpreting Culture-Dependent Memes](https://mlsanity.com/paper/2607.27798/) (2026)
- [FigmaTrace: Capturing Creative Nuances in Human Figma Design Workflows](https://mlsanity.com/paper/2608.21460/) (2026)

## Abstract (arXiv)

Internet memes represent a popular form of multimodal online communication and often use figurative elements to convey layered meaning through the combination of text and images. However, it remains largely unclear how multimodal large language models (MLLMs) combine and interpret visual and textual information to identify figurative meaning in memes. To address this gap, we evaluate eight state-of-the-art generative MLLMs across three datasets on their ability to detect and explain six types of figurative meaning. In addition, we conduct a human evaluation of the explanations generated by these MLLMs, assessing whether the provided reasoning supports the predicted label and whether it remains faithful to the original meme content. Our findings indicate that all models exhibit a strong bias to associate a meme with figurative meaning, even when no such meaning is present. Qualitative analysis further shows that correct predictions are not always accompanied by faithful explanations.