Chain of Thought Prompting

Chain-of-thought (CoT) prompting is a technique that enhances large language models' (LLMs) reasoning abilities by guiding them to solve problems step-by-step, mimicking human thought processes. Current research focuses on improving CoT prompting's effectiveness through various methods, including iterative refinement, contrastive learning, and the integration of external knowledge sources, often applied to models like GPT-3/4 and Llama. This approach significantly impacts LLM performance across diverse tasks, from mathematical problem-solving and question answering to more complex domains like planning and clinical reasoning, leading to more reliable and interpretable AI systems.