🌳 Tree-of-Thought Prompting: A Fresh Take on Boosting ChatGPT's Reasoning 💡

Have you ever wondered if a machine can reason like a human, exhibit superior intelligence, and even rectify its own mistakes? Enter Tree-of-Thought (ToT) Prompting. A game-changing technique that builds on the well-established Chain-of-Thought (CoT) prompting concept, making our favorite AI – ChatGPT, even smarter! 🚀

🧩 Solving Complex Puzzles with Large Language Models

Large Language Models (LLMs) often find themselves in a pickle when faced with complex reasoning problems. Let's illustrate with an example. 🎈

Imagine Bob carrying a cup. He walks around, places a ball in the cup, turns it upside down, and eventually leaves the cup in the garden. Now, where's the ball? If you said "in the bedroom", give yourself a pat on the back! But our buddy ChatGPT 3.5 incorrectly suggests the garden, while the more advanced ChatGPT 4 gets it right. Can we help ChatGPT 3.5 ace this without an upgrade? Yes, we can! 🎯

🪢 Tying the Knot with Chain-of-Thought Prompting

The concept of Chain-of-Thought prompting (CoT) has been nudging LLMs towards better reasoning since its introduction in a 2022 paper. But even with a prompt asking ChatGPT to think carefully and logically, it still misplaces our ball in the garden. 🌳

In its explanation, ChatGPT 3.5 brilliantly realizes the ball falls out when Bob flips the cup but still insists the ball is in the garden. It’s like ChatGPT is that friend who's super smart but somehow always loses their keys. 🔑

🌳 Branching Out with Tree-of-Thought Framework

Recent advances have given rise to the Tree-of-Thoughts (ToT) framework that encourages LLMs to explore multiple reasoning paths and self-correct as they progress. But these papers require multiple calls to an LLM, typically in Python. Isn't there a simpler way? Well, buckle up, because we're about to dive headfirst into a world of ToT-style prompts. 🚀

🗨️ The Magic of Tree-of-Thought Prompting

ToT-style prompting allows us to prompt ChatGPT 3.5 into imagining a collective reasoning effort of three different experts, each jotting down a step of their thinking. The catch? If any expert realizes they're wrong at any point, they bow out. It's like a game of intellectual musical chairs! 🎶

With this prompt, ChatGPT 3.5 solves the puzzle! It correctly places the ball in the bedroom! 🏆 But we're just getting started.

🧠 Example Prompt (chatGPT3.5)

Imagine three different experts are answering this question. All experts will write down 1 step of their thinking, then share it with the group. Then all experts will go on to the next step, etc. If any expert realises they're wrong at any point then they leave. The question is: It takes 1 hour to dry my shirt in the sun. I have 5 shirts. How long does it take to dry my shirts?

Response: Expert 1: Step 1: If it takes 1 hour to dry one shirt in the sun, we can assume that all shirts will take the same amount of time to dry.

Expert 2: Step 2: Since there are 5 shirts, and each shirt takes 1 hour to dry, we can calculate the total drying time by multiplying the drying time of one shirt by the number of shirts. Therefore, it would take 1 hour * 5 shirts = 5 hours to dry all the shirts.

Expert 3: Step 3: Hold on, I think Expert 2 made a mistake. While it's true that each shirt takes 1 hour to dry, we can dry multiple shirts simultaneously. If we assume that all shirts can be dried together in the sun, then it would still take 1 hour to dry all the shirts. The time required doesn't increase linearly with the number of shirts since they can dry concurrently.

Expert 2: Step 4: You're right, Expert 3. I apologize for the oversight. If we can dry all the shirts together, then it would indeed take 1 hour to dry all 5 shirts in the sun.

Expert 1: Step 5: Agreed. So the final answer is that it takes 1 hour to dry all 5 shirts in the sun, assuming they can be dried together simultaneously.

🛠️ Limitations and Enhancements

Though our approach shows promise, it's still in its early stages. Plus, ToT is a more complex framework than demonstrated in our example. But fear not! This opens up exciting opportunities to refine our prompts for even better results. Remember, Rome wasn't built in a day, and neither will the perfect AI be. 🏗️

🎯 Additional Perspectives

Our Tree-of-Thought framework isn't just a fun AI technique; it mirrors organizational decision-making involving multiple stakeholders. By adopting more organizational structures, we could potentially enhance the performance of LLMs. But that's a topic for another day. 🚀

🎈 Wrapping Up

Tree-of-Thought Prompting is a fresh and exciting development in the world of AI and Large Language Models. By providing our LLMs with the opportunity to reason collectively and correct their own mistakes, we're inching closer to creating machines that can reason with the best of us. And who knows, maybe one day, they'll be telling us where we left our keys! 🗝️