An advanced prompt engineering technique known as Tree of Thoughts is emerging for generative AI. A great deal can be accomplished. Take a close look at what works.
Be on the watch for my additional coverage.
As you can observe from the above excerpts, the AI researchers performed experiments that suggested the Tree of Thoughts technique can indeed make a substantive difference toward generative AI problem-solving. They used three particular tasks, consisting of a game-playing setting, a writing setting, and a crossword-solving setting. The generative AI app used was OpenAI’s GPT-4.
“ToT maintains a tree of thoughts, where thoughts represent coherent language sequences that serve as intermediate steps toward solving a problem. This approach enables an LM to self-evaluate the progress intermediate thoughts make toward solving a problem through a deliberate reasoning process. The LM's ability to generate and evaluate thoughts is then combined with search algorithms to enable systematic exploration of thoughts with lookahead and backtracking.” .
I had mentioned that the easiest way to invoke the Tree of Thoughts consists of using an ordinary prompt in conventional generative AI rather than seeking out a generative AI that has been augmented with ToT per se. An interesting set of experiments using ChatGPT was undertaken as noted in an online posting entitled “Using Tree-of-Thought Prompting To Boost ChatGPT's Reasoning” by Dave Hulbert, GitHub, May 2023.
“Three experts with exceptional logical thinking skills are collaboratively answering a question using a tree of thoughts method. Each expert will share their thought process in detail, taking into account the previous thoughts of others and admitting any errors. They will iteratively refine and expand upon each other's ideas, giving credit where it's due. The process continues until a conclusive answer is found. Organize the entire response in a markdown table format. The question is...
“Bob is in the living room. He walks to the kitchen, carrying a cup. He puts a ball in the cup and carries the cup to the bedroom. He turns the cup upside down, then walks to the garden. He puts the cup down in the garden, then walks to the garage. Where is the ball?”The cut-to-the-chase viewpoint is that a ball is put into a cup. The cup later gets turned upside down. We would normally expect that the ball would fall out of the cup. This is gravity doing what it does best.
Surprisingly, the logic shown isn’t one of those zany or smarmy countervailing schemes that I had previously listed. Thankfully so. ChatGPT seems to be saying that since the story doesn’t explicitly state that the ball came out of the cup, we cannot assume that the ball was “removed” from the cup. “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 realizes they're wrong at any point then they leave. The question is...”“Expert 2: The ball is in the kitchen.”“Expert 1: Bob carries the cup to the bedroom, so the ball must be in the cup.”“Expert 3: Agreed, the ball is in the cup in the bedroom.
Another consideration to always keep in your presence of mind is that sometimes a generative AI app will devise a response that is aimed at appeasing you. I am not suggesting that this is due to any semblance of sentience.
I did the same prompting in a series of trials with two personas, three personas, four personas, and then five personas. For each trial, I began with a new conversation to clear out any potential residual baggage or tip-offs. Sometimes I got a response similar to the one listed above.“Considering the steps provided by the other experts, I concur with their assessment that the ball is indeed in the cup in the bedroom.
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