Chain of Thought & Tree of Thought

Understanding AI ‘Hallucination’ and How to Manage It

Let’s talk about an intriguing aspect of AI, particularly the Large Language Models (LLMs) like AI Chat models.

Sometimes, these AI models encounter what we could call a “Hallucination” issue. It’s as if the AI starts daydreaming when unsure of the answer, leading to made-up but inaccurate responses. So how do we ensure AI Chat models don’t wander off into the realm of fiction? Here are three strategies:

Prompt Pipelines

A prompt pipeline is like giving AI Chat models a roadmap to understand your question. It works like this:

User: “Tell me about Mars.”

Without a prompt pipeline, the AI Chat model might be unsure about what aspect of Mars you’re interested in.

AI Chat model: “Mars could refer to the Roman god of war or the fourth planet from the Sun in our solar system. Could you please provide more details?”

Now, with a prompt pipeline in play:

User: “I would like to know more about Mars, the planet, particularly its potential for human habitation.”

AI Chat model: “Mars, often called the ‘Red Planet,’ is the fourth planet from the Sun in our solar system. It’s of great interest to scientists due to its similarities with Earth and its potential for future human habitation. Scientists have explored the idea of living on Mars due to a few promising factors. It has a day-night cycle similar to Earth’s, with seasons and polar ice caps made from water and carbon dioxide. However, there are many challenges, such as extreme temperatures, lack of oxygen, and exposure to harmful radiation. Current technologies, such as the ones being developed by NASA and private companies like SpaceX, aim to overcome these challenges and make human life possible on Mars in the future.”

Natural Language Reasoning

Natural Language Reasoning helps the AI Chat models think logically and sequentially, just like a human would. It assists the model in following a chain of thoughts, consequently enhancing the accuracy of its responses. Here’s a more comprehensive example using a business context:

First Prompt User: “My company is facing a sales slump. Can you suggest ways to tackle this issue?”

Without reasoning, the AI Chat model might offer a generic response.

AI Chat model: “Consider improving your marketing strategies and offering discounts.”

However, when we apply Natural Language Reasoning, it encourages the AI Chat models to provide a more detailed and comprehensive response:

Second Prompt User: “We’re a tech start-up specializing in creating personalized learning apps. Recently, we’ve noticed a decrease in our sales. Can you suggest specific strategies to address this issue?”

AI Chat model: “Given your industry and specific scenario, there are a few strategies you could explore. First, understanding your user behavior can be vital. In-app analytics tools can help you identify patterns and preferences among your user base. Secondly, consider optimizing the user experience based on these insights. Frequent updates with improved features, resolving bugs quickly, and ensuring a seamless user interface can enhance user satisfaction and promote sales. Thirdly, partnerships with educational institutions or companies might be beneficial for increasing your user base. Lastly, effective targeted marketing based on your audience demographics and interests can increase the visibility of your app. However, remember that every situation is unique, and these strategies should be adapted based on your specific circumstances.”

This kind of sequential thinking, known as ‘process supervision,’ significantly improves the accuracy of AI Chat models. It involves guiding the AI through each step of its thought process to verify its correctness. But remember, it’s not an automatic process. It requires human input to keep the AI Chat models on track.

So, there you have it! Now you’re familiar with the little daydreaming quirks of AI Chat models and how we handle them. It’s a fascinating world.