What is Tree of Thoughts (ToT) in LLM?

Prompting techniques can help you enhance the results obtained from Large Language Models or LLMs. You can rely on simple prompts to interact with LLMs for different tasks. On the other hand, advanced prompting techniques such as tree of thoughts in LLM training can improve the quality of results. The primary objective of advanced prompting techniques revolves around increasing the quality of prompts with more information for the LLM. 

You can leverage new prompting techniques such as tree of thoughts or TOT prompting to empower LLMs to solve new and unprecedented tasks. At the same time, you might also have some doubts regarding the similarity between chain-of-thought prompting and tree of thoughts. Let us find some important insights to understand the significance of Tree of Thoughts or TOT in prompt engineering.

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Understanding the Necessity of Tree of Thoughts

Large language models are great at understanding natural language queries, performing tasks such as language translation, text or audio generation, and answering questions. However, using LLMs for tough tasks such as mathematics problems, common reasoning tasks, or queries that involve understanding symbols and context can be extremely difficult. 

Therefore, it is important to find answers to “What is the tree of thoughts concept?” as it helps LLMs perform complex tasks. Some people assume that chain-of-thought prompting is the ideal solution for prompting in complex cases. It involves breaking down larger problems into smaller tasks, thereby helping LLMs perform complex tasks with ease. 

While chain-of-thought prompting presents promising advantages, it has a huge flaw that prompt engineers are likely to miss frequently. If the first thought is out of place, then the rest of the chain would also follow the wrong path. Sometimes, it is important to explore different threads of thought from the beginning. 

You would find some dead-end thoughts and dismiss them, after which you can come back to other threads that you can pursue. Subsequently, you would find different unexplored paths before ultimately rounding up on the perfect solution. You can notice that such an approach resembles the structure of a tree rather than a chain. 

Definition of Tree of Thoughts Concept

The reasons for introducing a tree of thoughts in prompt engineering show that the approach for branching out thought processes in LLMs can empower them for complex tasks. The review of tree of thoughts in LLM examples shows that the TOT prompting technique draws inspiration from the working of the human mind for solving complex reasoning tasks. TOT works by guiding the LLM through different ideas and helping with the re-evaluation of the ideas when required to achieve the desired solution. 

The concept of the tree of thoughts is better than chain-of-thought prompting as it does not rely on only one chain of thoughts. Therefore, the success of tree of thought prompting does not depend on the correctness and relevance of the first thought. On the other hand, using a tree of thoughts involves custom algorithms and lots of code to navigate through the layers and identify the ideal reasoning paths. The tree of thoughts concept powers up LLMs with the ability to reach decisions by accounting for different paths and evaluating the alternatives to determine the next course of action. 

Different Perspectives on Tree of Thoughts Prompting

Chain of thought prompting is an effective prompt engineering technique that can help you solve complex tasks. On the other hand, the domain of prompt engineering also involves many other spin-offs that follow a similar concept. However, a tree of thought prompting example shows that it is different from chain-of-thought prompting. At the same time, you will find two prominent perspectives on the concept of the tree of thoughts. 

The co-founder of Theta Labs explains tree of thoughts prompting as a rule-based control mechanism to facilitate systematic guidance of LLMs for solving Sudoku puzzles. In this description, LLMs can use tree of thoughts to classify intermediate puzzle states as the ‘thought’ nodes of a search tree. The second perspective on tree of thoughts by Princeton University and Google Deepmind researchers combines LLMs with tree search algorithms. 

The two perspectives on tree of thoughts in LLM use cases focus on one specific goal, i.e., enhancing the problem-solving capacity of LLMs. You can notice that each approach allows LLMs to explore different reasoning paths through trees. Most importantly, the approach used by Princeton University and Google Deepmind researchers stands out for effectiveness due to comprehensive testing on three different tasks. 

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Tree of Thoughts Unravels the Limitations of Chain of Thought Prompts

The best way to understand the workings of tree of thoughts prompting for LLMs involves acknowledging the limitations of other prompting techniques. One of the hugely undermined aspects in responses to “What is the tree of thoughts concept?” is that it is an advanced prompting technique. You must know that an advanced prompting technique comes into play for complex tasks when techniques like chain-of-thought prompting fail. 

Let us assume a situation in which you have to solve a complex task, such as brain games or puzzles. You are most likely to approach the task with an overall goal in mind. Subsequently, you would choose the ideal route and pursue the path. If you don’t find the solution in the selected path, then you can retrace your steps to ground zero. Tree of Thoughts works by following a similar approach to solve problems by capitalizing on the capacity for backtracking and exploring different branches. 

Another notable inference from reviews of tree of thoughts in LLM examples reflects on the limitations associated with chain-of-thought prompting. For example, it shows that chain-of-thought prompting only follows one route down the thought process. On the other hand, conventional prompting methods do not have any system for oversight or backtracking to evaluate different options.      

Working Mechanism of Tree of Thoughts in Prompt Engineering

The working mechanism of tree of thoughts in LLM prompting focuses on addressing four important questions. Therefore, you are likely to find four distinct steps in the working of tree of thoughts prompts. Here is an outline of the four important steps in the working of tree of thoughts concept for prompting.

• Breaking down the Larger Problem

The first step in the working of tree of thoughts prompts involves breaking down the bigger problem into smaller tasks. You would need a thought decomposer in the tree of thought prompting, just like in chain-of-thought prompting, to break larger problems into small steps. 

• Generating Ideas or Thoughts 

Based on the existing thought, you have to create a combination of different paths to solve a problem. The second step deals with the thought generator and focuses on ensuring divergent paths that can help in achieving the desired results. 

• Evaluation of the State of Each Path 

The state evaluator is also an important aspect of the working mechanism of the concept of the tree of thoughts. It helps in evaluating the effectiveness of candidate ideas on each path diverging from the existing thought. The two most common methods for evaluating ideas include independent evaluation and voting across different ideas. 

Independent evaluation involves the LLM evaluating each idea by allocating a specific value or classification. The second approach, voting for different ideas, is applicable in scenarios where direct evaluation can be a challenging task. In the case of voting, the model would compare the different solutions and vote for the ideal one. 

• Navigation of the Problem 

The final stage in the working mechanism of tree of thoughts prompting involves systematic exploration of a tree of thoughts. The tree of thoughts concept focuses on navigation and evaluation of the problem with the help of search algorithms. You can find two useful search algorithms in any tree of thought prompting example, such as breadth-first search and depth-first search. The breadth-first search involves exploring the states at different levels, while depth-first search focuses on the promising states and facilitates backtracking. 

Does the Tree of Thoughts Concept Have any Limitations?

Tree of thoughts concept expands the ambit of prompting to cover a wide range of complex tasks. Rather than sticking to the conventional chain-of-thought prompting and its derivatives, tree of thoughts brings a revised approach to solving problems. However, tree of thoughts prompting also has certain limitations. 

For example, the intentional and deliberate approach of following multiple paths makes it more expensive than chain-of-thought prompting. On the other hand, the tree of thoughts approach requires prompt engineers to impose restrictions on LLMs. Prompt engineers have to guide the LLM, help it with the generation and evaluation of new ideas, and enable tree searches. 

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Final Words 

The introduction to tree of thoughts in LLM prompting provides a clear impression of their advantages. You must have noticed that the tree of thoughts concept overcomes the limitations of chain-of-thought prompting. With the concept of the tree of thoughts, you can have multiple branches of thoughts that an LLM might consider when solving problems. It is different from chain-of-thought prompting, which leverages a single thought to create a chain of ideas for solving a specific task. 

You can notice similarities between chain-of-thought prompting and a tree of thoughts, such as breaking down a bigger problem into smaller tasks. However, tree of thoughts prompting has an advantage in terms of the ability to design LLMs for complex tasks. Learn more about the practical implications of Tree of Thoughts prompting and discover its potential right now.