What is chain of thought prompting?

Chain of Thought (CoT) prompting is a technique for improving the reasoning capabilities of large language models (LLMs) by generating intermediate reasoning steps. This approach helps the LLM generate more accurate answers. CoT prompting can be effectively used in conjunction with few-shot prompting to achieve better results on complex tasks that require reasoning before responding.

CoT has several advantages, including requiring no fine-tuning of the model and providing interpretability, allowing users to learn from the LLM’s responses by observing the reasoning steps followed. It can also improve robustness when transitioning between different LLM versions, ensuring consistent performance.

The key essence of CoT prompting is that the LLM should be instructed to explain its reasoning before providing a final answer, which tends to yield more accurate results even with seemingly straightforward mathematical tasks. Setting the temperature to 0 during CoT prompting is recommended, as it facilitates greedy decoding, focusing on the most probable paths to derive the final answer.

In summary, CoT prompting enhances the ability of LLMs to reason through a problem by guiding them through a series of logical steps before arriving at a conclusion.