# MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation

arXiv: https://arxiv.org/abs/2403.14652 · HTML: https://mlsanity.com/paper/2403.14652/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 24 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Supervised Multimodal Bitransformers for Classifying Images and Text](https://mlsanity.com/paper/1909.02950/) (2019)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)
- [Detecting Harmful Memes and Their Targets](https://mlsanity.com/paper/2110.00413/) (2021)
- [Prompting for Multimodal Hateful Meme Classification](https://mlsanity.com/paper/2302.04156/) (2023)
- [Detecting and Understanding Harmful Memes: A Survey](https://mlsanity.com/paper/2205.04274/) (2022)
- [Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection](https://mlsanity.com/paper/2308.08088/) (2023)
- [Disentangling Hate in Online Memes](https://mlsanity.com/paper/2108.06207/) (2021)
- [Detecting Propaganda Techniques in Memes](https://mlsanity.com/paper/2109.08013/) (2021)
- [Decoding the Underlying Meaning of Multimodal Hateful Memes](https://mlsanity.com/paper/2305.17678/) (2023)
- [Language Models are Few-shot Learners for Prognostic Prediction](https://mlsanity.com/paper/2302.12692/) (2023)
- [On Explaining Multimodal Hateful Meme Detection Models](https://mlsanity.com/paper/2204.01734/) (2022)
- [Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset](https://mlsanity.com/paper/2107.04313/) (2021)
- [Dank Learning: Generating Memes Using Deep Neural Networks](https://mlsanity.com/paper/1806.04510/) (2018)
- [memeBot: Towards Automatic Image Meme Generation](https://mlsanity.com/paper/2004.14571/) (2020)
- [Multimodal and Explainable Internet Meme Classification](https://mlsanity.com/paper/2212.05612/) (2022)
- [Memeify: A Large-Scale Meme Generation System](https://mlsanity.com/paper/1910.12279/) (2019)

## Built on by (most cited first, 9 of 9)

- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics](https://mlsanity.com/paper/2509.12248/) (2025)
- [Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models](https://mlsanity.com/paper/2505.00150/) (2025)
- [From Perception to Punchline: Empowering VLM with the Art of In-the-wild Meme](https://mlsanity.com/paper/2512.24555/) (2025)
- [MER-Bench: A Comprehensive Benchmark for Multimodal Meme Reappraisal](https://mlsanity.com/paper/2603.15020/) (2026)
- [Memes-as-Replies: Can Models Select Humorous Manga Panel Responses?](https://mlsanity.com/paper/2602.15842/) (2026)
- [Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges](https://mlsanity.com/paper/2607.19011/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation](https://mlsanity.com/paper/2608.27127/) (2026)

## Abstract (arXiv)

Online memes have emerged as powerful digital cultural artifacts in the age of social media, offering not only humor but also platforms for political discourse, social critique, and information dissemination. Their extensive reach and influence in shaping online communities' sentiments make them invaluable tools for campaigning and promoting ideologies. Despite the development of several meme-generation tools, there remains a gap in their systematic evaluation and their ability to effectively communicate ideologies. Addressing this, we introduce MemeCraft, an innovative meme generator that leverages large language models (LLMs) and visual language models (VLMs) to produce memes advocating specific social movements. MemeCraft presents an end-to-end pipeline, transforming user prompts into compelling multimodal memes without manual intervention. Conscious of the misuse potential in creating divisive content, an intrinsic safety mechanism is embedded to curb hateful meme production.