  Recent advancements in deep learning have led to the development of powerful
language models (LMs) that excel in various tasks. Despite these achievements,
there is still room for improvement, particularly in enhancing reasoning
abilities and incorporating multimodal data. This report investigates the
potential impact of combining Chain-of-Thought (CoT) reasoning and Visual
Question Answering (VQA) techniques to improve LM's accuracy in solving
multiple-choice questions. By employing TextVQA and ScienceQA datasets, we
assessed the effectiveness of three text embedding methods and three visual
embedding approaches. Our experiments aimed to fill the gap in current research
by investigating the combined impact of CoT and VQA, contributing to the
understanding of how these techniques can improve the reasoning capabilities of
state-of-the-art models like GPT-4. Results from our experiments demonstrated
the potential of these approaches in enhancing LM's reasoning and
question-answering capabilities, providing insights for further research and
development in the field, and paving the way for more accurate and reliable AI
systems that can handle complex reasoning tasks across multiple modalities.
