mlsanityData to 14 Oct 2026

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models

arXiv 2505.00150 ↗First posted 30 Apr 2025cs.CV cs.AI cs.CLcited by 2

On the map: Multimodal LLMs / vision-language models → Social bias and fairness in VLMs

Abstract (arXiv)

The rapid evolution of social media has provided enhanced communication channels for individuals to create online content, enabling them to express their thoughts and opinions. Multimodal memes, often utilized for playful or humorous expressions with visual and textual elements, are sometimes misused to disseminate hate speech against individuals or groups. While the detection of hateful memes is well-researched, developing effective methods to transform hateful content in memes remains a significant challenge. Leveraging the powerful generation and reasoning capabilities of Vision-Language Models (VLMs), we address the tasks of detecting and mitigating hateful content. This paper presents two key contributions: first, a definition-guided prompting technique for detecting hateful memes, and second, a unified framework for mitigating hateful content in memes, named UnHateMeme, which works by replacing hateful textual and/or visual components. With our definition-guided prompts, VLMs achieve impressive performance on hateful memes detection task. Furthermore, our UnHateMeme framework, integrated with VLMs, demonstrates a strong capability to convert hateful memes into non-hateful forms that meet human-level criteria for hate speech and maintain multimodal coherence between image and text. Through empirical experiments, we show the effectiveness of state-of-the-art pretrained VLMs such as LLaVA, Gemini and GPT-4o on the proposed tasks, providing a comprehensive analysis of their respective strengths and limitations for these tasks. This paper aims to shed light on important applications of VLMs for ensuring safe and respectful online environments.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Social bias and fairness in VLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 39 of its 46 references that are in the map. First posted on arXiv 30 Apr 2025.

Builds on

39 of 46 references.

References · 39

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27 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Towards Training-free Multimodal Hate Localisation with Large Language Modelscited by 0
  2. 2026Beyond Accuracy: A Qualitative Analysis of Vision-Language Models for Hate Speech Detection in Memescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Exploring the Limits of Zero Shot Vision Language Models for Hate Meme Detection: The Vulnerabilities and their Interpretations
  2. 2025Demystifying Hateful Content: Leveraging Large Multimodal Models for Hateful Meme Detection with Explainable Decisions
  3. 2023Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model
  4. 2026FBHM: Functional Benchmarking and Steering of VLMs for Hateful Meme Detection
  5. 2023Mapping Memes to Words for Multimodal Hateful Meme Classification
  6. 2025Labels or Input? Rethinking Augmentation in Multimodal Hate Detection
  7. 2025Improving Multimodal Hateful Meme Detection Exploiting LMM-Generated Knowledge
  8. 2023A Review of Vision-Language Models and their Performance on the Hateful Memes Challenge