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arXiv · 2605.31349

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

Paramananda Bhaskar·Naquee Rizwan·Daksh Jogchand·Saurabh Kumar Pandey·Animesh Mukherjee
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Abstract

Hateful meme detection remains a formidable challenge for vision-language models, as existing benchmarks are structurally observational - confounding rhetorical hate mechanisms with target community features and preventing causal evaluation of model vulnerabilities. To address this, we introduce FBHM, a systematically curated benchmark of Functionality Based Hateful Memes constructed along two orthogonal axes: 25 distinct rhetorical functionalities and 10 target communities (5,000 memes total). Benchmarking state-of-the-art VLMs reveals a severe generalization gap: models highly accurate on standard datasets catastrophically drop to near-random performance on FBHM, proving they exploit dataset-specific heuristics rather than robust multimodal reasoning. To efficiently close this gap, we propose LSV (learnable steering vectors), an ultra-low data regime strategy that applies a causal intervention objective on as few as 500 steering samples (50 unique base memes), boosting FBHM performance by ~30 Macro-F1 points while outperforming in-context learning and PEFT without degrading source-domain performance.

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Paramananda Bhaskar, Naquee Rizwan, Daksh Jogchand, Saurabh Kumar Pandey, Animesh Mukherjee. 2026-08-29. FBHM: Functional Benchmarking and Steering of VLMs for Hateful Meme Detection. https://arxiv.org/abs/2605.31349

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