Tree of Thoughts Framework: A New Approach to Decision-Making with AI

In the ever-evolving world of artificial intelligence, new methodologies and frameworks are constantly emerging. Today, I want to introduce you to an exciting new concept in the world of language models, called the "Tree of Thoughts" (ToT) framework. Developed by researchers at DeepMind and Princeton University, this framework is a significant leap forward from the traditional prompting methods you might be familiar with, and it has the potential to revolutionize how we interact with language models.

The research team found that in controlled studies, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, their method achieved a success rate of 74%. And more importantly, it's not a technique reserved for laboratories. This is a practical strategy that can be used in our everyday business decision-making.

Prompting Techniques: Overview

As you may know, prompting is a fundamental way of interacting with language models. It involves providing an input (a "prompt") to the model, which then generates an output based on that input. This is the simplest form of interaction, often referred to as "input-output" prompting.

However, as we look more closely at the capabilities of language models, we find that this simple input-output interaction can be significantly expanded. This is where the concept of "chain of thought" prompting comes into play. Instead of a single input leading to a single output, we can guide the model through a series of interconnected thoughts, each one leading to the next. This allows for more complex and nuanced interactions, enabling the model to tackle more challenging tasks.

Taking it a step further, we can introduce the concept of "self-consistency" within chain of thought prompting. This involves the model considering multiple different chains of thought and evaluating the consistency of the outputs. This adds an additional layer of sophistication to the model's decision-making process, allowing it to make more informed and reliable choices.

Introducing Tree of Thoughts (ToT)

Now, imagine if we could take all these concepts and arrange them not just in a linear chain, but in a branching, interconnected structure - a "Tree of Thoughts". This is the essence of the ToT framework. It allows the model to explore multiple reasoning paths, backtrack when necessary, and make globally informed decisions. This approach significantly enhances the problem-solving abilities of language models, enabling them to tackle tasks that require non-trivial planning or search.

The Tree of Thoughts framework is a structured approach to decision-making that allows us to break down complex problems into manageable parts, explore various lines of inquiry, and make decisions based on a comprehensive understanding of the situation at hand. The framework is based on the concept of a "tree" of thoughts, where each branch represents a different line of inquiry or potential decision. By exploring each branch, we can gain a deeper understanding of the problem, consider various perspectives, and make informed decisions.

Applying the Tree of Thoughts Framework: A Step-by-Step Guide

The Tree of Thoughts framework is a powerful tool for decision-making, but how exactly do we apply it? Here are the steps to follow:

1. Thought Decomposition: Break down the decision into smaller thoughts. What are the key steps or considerations involved in this decision?

2. Thought Generation: At each step, generate a set of possible actions or "thoughts". What are the different options or strategies you could consider?

3. State Evaluation: Evaluate the potential outcomes of each thought. What are the potential benefits, costs, and risks associated with each option?

4. Search Algorithm: Use a method to explore the different paths of thoughts. This could be as simple as a rule of thumb or a decision-making strategy. How will you decide between the different options?

5. Backtracking: Consider the possibility of backtracking. If a chosen path doesn't seem to lead to the desired outcome, how could you adjust your strategy or explore a different path?

ToT Framework: An Illustration

To illustrate, let's apply the ToT framework to a decision about launching a new marketing campaign:

1. Thought Decomposition: "I'm considering launching a new marketing campaign. What are the key steps or 'thoughts' I should consider?"

2. Thought Generation: "What are some options for the new campaign?"

3. State Evaluation: "What are the potential outcomes of a social media campaign?"

4. Search Algorithm: "How should I decide between these options?"

5. Backtracking: "What if I choose an option and it doesn't work out?"

By following these steps, you can apply the Tree of Thoughts framework to any decision or problem you're facing. This structured approach allows you to explore different lines of inquiry, evaluate potential outcomes, and make informed decisions.

As we continue to navigate the complex landscape of AI, the Tree of Thoughts framework can serve as a valuable tool for enhancing our decision-making process. By embracing this framework, we can make more informed decisions, harness the power of AI more effectively, and create a brighter future for our businesses and our society.

In the spirit of learning and growth, I invite you to apply the Tree of Thoughts framework to a problem or decision you're currently facing. Experiment with this tool, explore different lines of inquiry, and discover the power of structured decision-making. I look forward to hearing about your experiences and insights.

*Source: "Tree of Thoughts: Deliberate Problem Solving with Large Language Models" by Yao, Yu, Zhang, Shafran, Griffith’s, Cal, and Narasimhan, published by DeepMind and Princeton University on May 17, 2023.*

I'm Lori Mazor and I teach AI with a human touch: empowering intelligent business. If you're interested in bringing Generative AI training to your employees, check out my?website?for course offerings or DM me on LinkedIn for more information.