  Large visual-language models (LVLMs) exhibit exceptional performance in
visual-language reasoning across diverse cross-modal benchmarks. Despite these
advances, recent research indicates that Large Language Models (LLMs), like
GPT-3.5-turbo, underachieve compared to well-trained smaller models, such as
BERT, in Fake News Detection (FND), prompting inquiries into LVLMs' efficacy in
FND tasks. Although performance could improve through fine-tuning LVLMs, the
substantial parameters and requisite pre-trained weights render it a
resource-heavy endeavor for FND applications. This paper initially assesses the
FND capabilities of two notable LVLMs, CogVLM and GPT4V, in comparison to a
smaller yet adeptly trained CLIP model in a zero-shot context. The findings
demonstrate that LVLMs can attain performance competitive with that of the
smaller model. Next, we integrate standard in-context learning (ICL) with
LVLMs, noting improvements in FND performance, though limited in scope and
consistency. To address this, we introduce the \textbf{I}n-context
\textbf{M}ultimodal \textbf{F}ake \textbf{N}ews \textbf{D}etection (IMFND)
framework, enriching in-context examples and test inputs with predictions and
corresponding probabilities from a well-trained smaller model. This strategic
integration directs the LVLMs' focus towards news segments associated with
higher probabilities, thereby improving their analytical accuracy. The
experimental results suggest that the IMFND framework significantly boosts the
FND efficiency of LVLMs, achieving enhanced accuracy over the standard ICL
approach across three publicly available FND datasets.
