  The linguistic capabilities of Multimodal Large Language Models (MLLMs) are
critical for their effective application across diverse tasks. This study aims
to evaluate the performance of MLLMs on the VALSE benchmark, focusing on the
efficacy of few-shot In-Context Learning (ICL), and Chain-of-Thought (CoT)
prompting. We conducted a comprehensive assessment of state-of-the-art MLLMs,
varying in model size and pretraining datasets. The experimental results reveal
that ICL and CoT prompting significantly boost model performance, particularly
in tasks requiring complex reasoning and contextual understanding. Models
pretrained on captioning datasets show superior zero-shot performance, while
those trained on interleaved image-text data benefit from few-shot learning.
Our findings provide valuable insights into optimizing MLLMs for better
grounding of language in visual contexts, highlighting the importance of the
composition of pretraining data and the potential of few-shot learning
strategies to improve the reasoning abilities of MLLMs.
