#Chain of Thought

This project investigates the role of chain-of-thought prompting in improving reasoning within large language models. It reviews diverse research papers, focusing on innovative methods like self-consistency and program-aided models. Noteworthy advancements include the use of language models in symbolic reasoning and scalable architectures such as PaLM. The project encompasses fundamental theories and practical implementations, addressing multi-context and multilingual capabilities, and aims to offer practical insights into potential real-world applications.

This project focuses on improving multimodal reasoning using advanced models such as GPT-4 and ChatGPT for enhanced scientific question answering. It employs the extensive ScienceQA dataset to drive innovations in scientific reasoning through the Chain-of-Thought approach. Widely recognized within the academic and AI sectors, it's featured in various prestigious publications. The project frequently updates with new model additions, maintaining its significance as a benchmark in AI, showcasing substantial progress in addressing intricate scientific questions.