aiqbench analysis
This research introduces PEST, a framework designed to detect, explain, and intervene on hateful memes using generative Vision-Language Models (VLMs) with few-shot learning capabilities. By integrating explanation and intervention directly into the detection pipeline, the study addresses real-world moderation challenges where large annotated datasets are unavailable. The work demonstrates significant potential for deploying generalizable AI safety mechanisms in production environments to handle toxic content efficiently.
PEST: Parameter Efficient Steering of Blackbox VLMs via Agentic Few-shot Alignment for Hateful Meme Moderation
arXiv:2601.04692v2 Announce Type: replace Abstract: In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene them before being posted - by applying a range of strategies built on top of generative AI models. To the best of our knowledge, explanation and intervention have typically been studied separately from detection, which does not reflect real-world conditions. Further, since curating large annotated datasets for meme moderation is prohibitively expensive, we propose a novel framework - PEST - that leverages task-specific generative VLMs and the few-shot adaptability of large VLMs to cater to different types of memes. We believe this is the first work focused on generalizable hateful meme moderation under limited data conditions, and has strong potential for deployment in real-world production scenarios. Warning: Contains potentially toxic contents.
Abstract:In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene them before being posted - by applying a range of strategies built on top of generative AI models. To the best of our knowledge, explanation and intervention have typically been studied separately from detection, which does not reflect real-world conditions. Further, since curating large annotated datasets for meme moderation is prohibitively expensive, we propose a novel framework - PEST - that leverages task-specific generative VLMs and the few-shot adaptability of large VLMs to cater to different types of memes. We believe this is the first work focused on generalizable hateful meme moderation under limited data conditions, and has strong potential for deployment in real-world production scenarios. Warning: Contains potentially toxic contents.
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2601.04692 [cs.CL]
(or arXiv:2601.04692v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2601.04692 Focus to learn more arXiv-issued DOI via DataCite
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