Large Language Models Are Zero Shot Reasoners

Zero-shot prompting asks a question without giving the LLM any other information. It can be unreliable because a word might have multiple meanings. For example, if you ask an LLM to "explain the different types of banks" it might tell you about river banks. Few-shot prompting gives the LLM an example or two before asking the question. This gives the LLM more context so it can give you a better answer. It can also help the LLM understand what format you want the answer in. Chain-of-thought prompting asks the LLM to explain how it got its answer. This helps you understand the LLM's reasoning process, which is an important part of Explainable AI (XAI). Chain-of-thought prompting can also help the LLM give a better answer by thinking about different possibilities. These three methods can all help you get better results from LLMs by providing more context or instructions.

There are no comments yet.

Please log in to write the first comment.