  Recent advances show that two-stream approaches have achieved outstanding
performance in hateful meme detection. However, hateful memes constantly evolve
as new memes emerge by fusing progressive cultural ideas, making existing
methods obsolete or ineffective. In this work, we explore the potential of
Large Multimodal Models (LMMs) for hateful meme detection. To this end, we
propose Evolver, which incorporates LMMs via Chain-of-Evolution (CoE)
Prompting, by integrating the evolution attribute and in-context information of
memes. Specifically, Evolver simulates the evolving and expressing process of
memes and reasons through LMMs in a step-by-step manner. First, an evolutionary
pair mining module retrieves the top-k most similar memes in the external
curated meme set with the input meme. Second, an evolutionary information
extractor is designed to summarize the semantic regularities between the paired
memes for prompting. Finally, a contextual relevance amplifier enhances the
in-context hatefulness information to boost the search for evolutionary
processes. Extensive experiments on public FHM, MAMI, and HarM datasets show
that CoE prompting can be incorporated into existing LMMs to improve their
performance. More encouragingly, it can serve as an interpretive tool to
promote the understanding of the evolution of social memes.
