Intermediate prompting

What is the primary purpose of Chain of Thought Prompting?

To prompt the model with complex questions

To allow the model to generate multiple correct answers

To break down the problem into smaller steps and elicit reasoning from the model

To improve the model's ability to provide relevant answers

In the Chain of Thought Prompting technique, what is the purpose of the second step?

To find the most relevant answers

To create a list of potential questions

To reduce the given problem to simpler problems

To create a list of potential answers

What is the main goal of Least-to-Most Prompting?

To improve the model's understanding of context

To enhance the model's creativity

To reduce the model's computation time

To improve the model's accuracy and generalization

In the context of few-shot learning, what is the role of exemplars in a prompt?

They are used to provide gold labels for the model to learn from

They help the model understand the label space and format of the answer

They provide detailed explanations of the problem

They are used to teach the model about new concepts

How many exemplars are generally recommended for few-shot prompts?

1-3

4-8

9-15

16-20

In the context of prompting, what is meant by "labels"?

The keywords used to describe the model's architecture

The names assigned to different categories or classes that the model should produce as outputs

The process of providing guidance to the model through text

The individual tokens generated by the model in response to a prompt

What is the "Least to Most Prompting" technique in the context of large language models?

A method of providing the least amount of information to a model to see its natural response

A technique that involves providing more examples in the prompt than usual

A strategy where simple problems are solved first, and then increasingly complex problems are tackled

A method where the model is only allowed to produce short answers

In Least-to-Most Prompting, what is the main purpose of the reduction step?

To make the problem more complex

To reduce the problem to simpler problems or components

To identify the most relevant answers

To enhance the model's understanding of the problem

What is the primary focus of Least-to-Most Prompting when compared to other prompting techniques?

Achieving higher accuracy in all types of problems

Reducing the complexity of the problem

Eliciting complex reasoning from large language models

Increasing the model's ability to generate lengthy answers

What is the primary goal of incorporating self-consistency in prompting strategies?

Ensuring that the language model can solve complex problems

Simplifying the task for the language model

Encouraging the language model to provide consistent and coherent answers

Improving the format of the prompts

How can self-consistency be achieved when designing prompts for a language model?

By providing multiple prompts that ask the same question in different ways

By breaking down the task into smaller subproblems

By using exemplars to demonstrate the desired response format

By focusing on the least-to-most prompting technique

In the context of prompting, what does 'Generated Knowledge' refer to?

The knowledge acquired by the AI model during training

The knowledge created by the user while interacting with the AI model

The knowledge synthesized by the AI model based on its understanding of the input prompt

The knowledge provided by the user as input to the AI model

Intermediate prompting

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