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.

Financial reports offer critical insights into a company's operations, yet their extensive length--typically spanning 30-40 pages--poses challenges for swift decision-making in dynamic markets. To address this, we leveraged fine-tuned Large Language Models (LLMs) to distill key indicators and operational metrics from these reports basis questions from the user. We achieved results comparable to baseline on the final numerical answer, a competitive accuracy in numerical reasoning and calculation. Analyzing financial reports serves as a powerful tool for various stakeholders to gain crucial insights into a company's performance and health Gupta et al. (2021). Investors rely on these reports to assess the company's profitability, growth potential, and risk levels, aiding their investment decisions. For management, these reports offer a window into operational efficiency, helping in strategic planning and identifying areas for improvement. Creditors and lenders use this data to gauge a company's ability to meet financial obligations and assess lending risks.