FBHM: Functional Benchmarking and Steering of VLMs for Hateful Meme Detection

arXiv:2605.31349 · cs.CL, cs.AI, cs.CV, cs.MM · Submitted 2026-05-29 · Read on arXiv

cs.CL, cs.AI, cs.CV, cs.MM

Submitted: 2026-05-29

Updated: 2026-08-29

Code: https://github.com/MaartenGr/BERTopic

Terminology

• Steering — 25× in this paper · explained in 2 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Alignment — 15× in this paper · explained in 1 episode
• Benchmark — 14× in this paper · explained in 2 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Benchmarking — 6× in this paper · explained in 3 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Multimodal Reasoning — 5× in this paper · explained in 5 episodes
• Entanglement — 4× in this paper · explained in 3 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Structural Reasoning — 4× in this paper · explained in 2 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Functionality Based Hateful Memes — 3× in this paper
• In-Context Learning — 3× in this paper · explained in 4 episodes
• Activation Steering — 2× in this paper · explained in 4 episodes
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Facebook Hateful Memes — 2× in this paper
• Personally Identifiable Information — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Arbiter — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection
• QLoRA: Efficient Finetuning of Quantized LLMs
• What matters when building vision-language models?
• In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
• Read as You See: Guiding Unimodal LLMs for Low-Resource Explainable Harmful Meme Detection
• STEMTOX: From Collaborative Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning
• The Enforcement and Feasibility of Hate Speech Moderation
• VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations

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