Deliberate Problem Solving with Large Language Models

📝The paper introduces a decoding technique for using large language models that involves an explicit tree search over model outputs.

🌈The technique is useful for tasks that require stepwise investigation and problem solving.

🤔The paper proposes new tasks where the decoding technique performs well.

📝Chain of Thought prompting is a similar technique that instructs the model to explicitly make intermediate steps in problem solving.

🎯The paper suggests combining Chain of Thought prompting with the proposed decoding technique for better results.

What is the main focus of this paper?

—The main focus of this paper is to introduce a decoding technique for using large language models that involves an explicit tree search over model outputs.

What type of tasks can benefit from the proposed decoding technique?

—Tasks that require stepwise investigation and problem solving can benefit from the proposed decoding technique.

Are there any other techniques similar to Chain of Thought prompting?

—Yes, Chain of Thought prompting is a similar technique that instructs the model to explicitly make intermediate steps in problem solving.

Can the proposed decoding technique be combined with Chain of Thought prompting?

—Yes, the paper suggests combining Chain of Thought prompting with the proposed decoding technique for better results.

What are some potential applications of the decoding technique?

—The decoding technique can be applied to various tasks that require stepwise investigation, such as problem solving, story generation, and more.

00:00The paper proposes a decoding technique for using large language models that involves an explicit tree search over model outputs.

01:20The technique is useful for tasks that require stepwise investigation and problem solving.

04:20The paper suggests combining Chain of Thought prompting with the proposed decoding technique for better results.