Chain of Thought

Chain of Thought (CoT) prompting enhances the reasoning abilities of large language models (LLMs) by encouraging them to generate intermediate reasoning steps before arriving at a final answer. Current research focuses on improving CoT's effectiveness through techniques like multi-perspective verification, incorporating external knowledge (e.g., symbolic knowledge or multi-modal information), and optimizing the efficiency of the reasoning process (e.g., through compressed representations or adaptive sampling). This work is significant because it addresses limitations in LLMs' reasoning capabilities, leading to improved performance on complex tasks across diverse domains, including question answering, translation, and even medical diagnosis.