Analyzing the Power of Chain of Thought through Memorization Capabilities

Recent research highlights the chain of thought (CoT) technique as a promising approach to enhance large language models' mathematical reasoning capabilities. Studies indicate that CoT can improve performance on complex mathematical problems by enabling models to generate intermediate reasoning steps. However, the extent to which CoT benefits transformers across the full spectrum of reasoning tasks remains uncertain and is currently under investigation. While initial findings suggest improvements in specific domains, it is not yet clear if these gains generalize to all types of reasoning challenges faced by large language models. Ongoing analysis aims to clarify whether CoT consistently enhances model performance or if its advantages are limited to particular problem categories. This nuanced understanding is crucial for developing more robust and versatile AI reasoning systems. The evolving research landscape continues to explore the potential and limitations of CoT within the broader context of transformer-based models.