Unlocking Reasoning with Chain-of-Thought Prompting By Jill Smith

Chain-of-thought prompting enhances the reasoning capabilities of large language models (LLMs) by guiding them through intermediate steps. This method, where a series of reasoning demonstrations are included as exemplars, reveals how advanced reasoning emerges naturally in sufficiently large models. Experiments across multiple LLMs demonstrate significant improvements in tasks requiring arithmetic, commonsense, and symbolic reasoning. Notably, a 540B-parameter model achieves state-of-the-art accuracy on the GSM8K math benchmark using just eight chain-of-thought examples, outperforming even fine-tuned GPT-3 with a verifier. This approach highlights the transformative potential of thoughtful prompting to unlock complex problem-solving capabilities in LLMs.