GPT is an incredibly powerful tool, but did you know you can supercharge its capabilities with the right prompt? One such trick is called The Chain of Thought method, a technique with over 1,000 citations to date. Here’s how it works:
Imagine you have a question you want to ask GPT or any other language model. We’ll call this your test input. Researchers have observed that GPT often performs better when you precede your test input with an example which includes both a question and its answer. This preceding example serves as an in-context guide, essentially showing GPT what kind of response you’re looking for.
However, when the task at hand is particularly complex, GPT can still struggle to produce accurate answers. To assist language models in handling more intricate tasks, researchers have developed a prompting strategy known as The Chain of Thought. The secret sauce here is in the details. Researchers manually add steps one should take to solve the example question within the in-context guide. By doing so, the language model understands that it’s expected to approach the problem in a step-by-step manner. This enables the model to generate a series of steps before arriving at the final and often more accurate answer.
So why does the Chain of Thought method work so effectively? Well, unlike humans, language models don’t have the ability to think internally. They generate each subsequent word based solely on the words that have come before it. In standard prompting settings, the model is, in a sense, rushed to produce an answer without a thoughtful process. In contrast, the Chain of Thought essentially allows the language model to think out loud, converting its thought process into words. This enables the model to build upon its own generated text, creating a more reasoned and often more accurate final answer.
However, the traditional Chain of Thought technique involves manually crafting demonstrations with each step laid out. Isn’t there a more streamlined approach? Good news! A group of researchers has found an alternative. They discovered that you can prompt language models to generate step-by-step solutions without a demonstration simply by including the phrase ’let’s think step by step’ in your prompt.
This ingenious workaround is known as zero-shot Chain of Thought. Are you eager to try out this technique for your own use case? If you’re starting from scratch, there are essential tasks you’ll need to tackle, such as setting up an evaluation pipeline, managing test cases, and tracking performance. But the good news is, you don’t have to go it alone.
Introducing Prompt Lab, a platform that takes care of all these complexities for you, offering a ready-to-use solution to supercharge your language model experience. Now let’s dive into a step-by-step demonstration using the zero-shot Chain of Thought prompt technique.
First things first, we’ll create a new project. Think of this as the container where we’ll store all the prompts, test cases, and metrics we want to experiment with. You have options here: select chat mode if your end goal is to create a chatbot, or opt for text mode if you’re aiming to build a machine learning tool. To keep things tidy, don’t forget to add some notes and give your project a memorable name.
And now, the initial task at hand: adding your first batch of test cases for this particular project. Let’s put GPT to the test with two questions that ideally should be solved through a step-by-step approach. The first asks the language model to determine which is greater: Pi or 3.4. The second prompts it to identify the color of an object after several shuffles have occurred.
In this demonstration, we include an expected answer for each test case input. By comparing the language model’s generated response with this expected answer, we’re able to accurately gauge the effectiveness of our prompt.
The next step involves adding prompt templates. The system will merge the test case input with these templates to obtain the actual input for the language model. This modular approach ensures that both test cases and prompt strategies can be reused efficiently.
Let’s see how a basic zero-shot prompt performs. For this experiment, we’ll employ GPT4, the most advanced language model available to date. For this basic prompt, the template is just a ’test case_input’ placeholder, which means we’re using the test case input directly as the prompt without any additional instructions to guide the model.
To evaluate the model’s performance, we’ll use a metric known as the Rouge recall score. This score measures the similarity between the expected answer and the language model’s generated response by quantifying how many words from the expected answer appear in the generated output. While you could certainly evaluate performance manually by simply eyeballing each case, using automated metrics offers a more objective assessment, especially crucial for large-scale evaluations.
As it turns out, the basic zero-shot prompt didn’t fare well, even with the powerful GPT4 behind it. The answers were incorrect for both test cases, and the Rouge recall score reflected this with a low value.
Now, let’s give zero-shot Chain of Thought a try. By simply incorporating the phrase ’think step by step’ into the prompt, GPT4 begins to generate a logical series of steps for each test case prior to arriving at its final answer. The result? A far more accurate output, which is corroborated by a perfect Rouge recall score of 1.0.
So there you have it, a simple demonstration to get you started. Ready to embark on your own journey? Sign up at Prompt Lab and kickstart your experience with free monthly credits.
