Today, I found a very interesting paper called “Tree of Thoughts (ToT)”(1). With ToT, we can solve the tasks, where we could not do it before. So I want to share it with you and consider how it works together. Let us start now!

1.Chain of Thoughts (CoT)

This paper provides four kinds of prompting as the chart below says. The left one is called “IO prompting” and is relatively simple. The right one is the most complex, called “Tree of Thoughts (ToT)”.

Among four kinds of prompting, I focus on Chain of Thoughts (CoT) first because it gives us a fundamental space to explore. The paper says “The key idea is to introduce a chain of thoughts z1, · · · , zn to bridge x and y, where each zi is a coherent language sequence that serves as a meaningful intermediate step toward problem solving“. By CoT, we explore a prompting method for improving the reasoning abilities of LLMs and solve complex tasks effectively. Once we understand how CoT works, let us move on ToT.

2. Tree of Thoughts (ToT)

Let us expand CoT with tree search so that we can apply it to more complex tasks effectively. This paper says “we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving.”. Sounds great! OK, let us consider how it works.

ToT has four steps to implement it. I would like to explain them step by step.

decompose the process into thoughts
- each thought should be small enough so that LLMs can generate promising and diverse samples
generate states
- generate potential thoughts from each state. There are two kinds of methods to do this according to this paper.
evaluate each state
- LLMs evaluate each state to decide how a tree should grow
search for the best state
- If the current state is not good enough, we should search into other branches. There are several search algorithms to do that.

3. ToT can be solved by MCTS

Although ToT can be solved with relatively simple Tree Search algorithms, we can use more advanced algorithms, such as Monte Carlo Tree Search (MCTS). It has been famous since AlphaGo defeated a professional human Go player in March 2016. In AlphaGo, MCTS is combined with Neural network. This is sometimes called “model guided Tree Search” and we do not need to search for the whole possible state anymore. In the picture, Demis Hassabis, Google DeepMind CEO, explained how it works(2).

It must be exciting when ToT can be searched by MTCS in the near future as wider and deeper states can be explored and it must provide us good results.

Thanks for your attention! I would like to follow the progress of ToT and share it with you soon. Stay tuned!

1) “Tree of Thoughts: Deliberate Problem Solving with Large Language Models” Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan, 17 May 2023, https://arxiv.org/abs/2305.10601

2) Using AI to Accelerate Scientific Discovery | Campus Lecture with Demis Hassabis, https://www.youtube.com/watch?v=Ds132TzmLRQ&t=1381s

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