The article also introduces Zero-Shot CoT prompting, which adds "Let's think step by step" to the original prompt to guide the model's reasoning process. Another variant, Automatic CoT, generates intermediate reasoning steps automatically using a database of diverse questions. The article also discusses the potential of Multimodal CoT prompting, which uses both words and pictures to guide the model. However, it notes that CoT prompting does not guarantee correct reasoning paths and can lead to both correct and incorrect answers. The article concludes by suggesting the use of Vellum.ai's tools for experimenting with different CoT prompts and models.
Key takeaways:
- Chain-of-Thought (CoT) prompting is a technique that guides Large Language Models (LLMs) to explain their reasoning process when dealing with complex problems. It involves showing the model examples where the step-by-step reasoning is clearly laid out.
- CoT prompting differs from Few-Shot prompting in that it focuses on showing the step-by-step thinking process, not just the final answer. It's particularly useful for tasks that require complex reasoning and works well with larger models.
- Zero-Shot Chain-of-Thought prompting and Automatic Chain of Thought (Auto-CoT) are variations of CoT prompting. The former involves adding "Let's think step by step" to the original prompt, while the latter automatically generates intermediate reasoning steps using a database of diverse questions.
- Despite its benefits, CoT prompting has limitations, including the lack of guarantee for correct reasoning paths. Other techniques like Self-Consistency and Tree of Thoughts (ToT) can be used to address these limitations.