# Labels or Input? Rethinking Augmentation in Multimodal Hate Detection

arXiv: https://arxiv.org/abs/2508.11808 · HTML: https://mlsanity.com/paper/2508.11808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 16 of 16)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Visual Transformers: Token-based Image Representation and Processing for Computer Vision](https://mlsanity.com/paper/2006.03677/) (2020)
- [SmolVLM: Redefining small and efficient multimodal models](https://mlsanity.com/paper/2504.05299/) (2025)
- [InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output](https://mlsanity.com/paper/2407.03320/) (2024)
- [Qwen2.5-1M Technical Report](https://mlsanity.com/paper/2501.15383/) (2025)
- [Visual Prompting in Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2409.15310/) (2024)
- [A Multimodal Framework for the Detection of Hateful Memes](https://mlsanity.com/paper/2012.12871/) (2020)
- [Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models](https://mlsanity.com/paper/2405.10431/) (2024)
- [CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries](https://mlsanity.com/paper/2501.01282/) (2025)
- [Hate Personified: Investigating the role of LLMs in content moderation](https://mlsanity.com/paper/2410.02657/) (2024)
- [SAFE-MEME: Structured Reasoning Framework for Robust Hate Speech Detection in Memes](https://mlsanity.com/paper/2412.20541/) (2024)
- [From Recognition to Reasoning: Advancing Multimodal Harmful Meme Detection via Chain-of-Thought Alignment](https://mlsanity.com/paper/2506.18919/) (2025)
- [TRACE: Textual Relevance Augmentation and Contextual Encoding for Multimodal Hate Detection](https://mlsanity.com/paper/2504.17902/) (2025)
- [Cultural Awareness in Vision-Language Models: A Cross-Country Exploration](https://mlsanity.com/paper/2505.20326/) (2025)

## Abstract (arXiv)

Online hate remains a significant societal challenge, especially as multimodal content enables subtle, culturally grounded, and implicit forms of harm. Hateful memes embed hostility through text-image interactions and humor, making them difficult for automated systems to interpret. Although recent Vision-Language Models (VLMs) perform well on explicit cases, their deployment is limited by high inference costs and persistent failures on nuanced content. This work examines how far small models can be improved through prompt optimization, fine-tuning, and automated data augmentation. We introduce an end-to-end pipeline that varies prompt structure, label granularity, and training modality, showing that structured prompts and scaled supervision significantly strengthen compact VLMs. We also develop a multimodal augmentation framework that generates counterfactually neutral memes via a coordinated LLM-VLM setup, reducing spurious correlations and improving the detection of implicit hate. Ablation studies quantify the contribution of each component, demonstrating that prompt design, granular labels, and targeted augmentation collectively narrow the gap between small and large models. The results offer a practical path toward more robust and deployable multimodal hate-detection systems without relying on costly large-model inference.